Hiroshi Motoda
Summary
Hiroshi Motoda is Professor Emeritus of Osaka University and now is retired. He was ascientiﬁc advisor at AFOSR/AOARD (Asian Oﬃce of Aerospace Research and Development, Air Force Oﬃce of Scientiﬁc Research, US Air Force Research Laboratory) since2006 till 2018, and worked as an international program oﬃcer. Before that, he was aprofessor at the department of Advanded Reasoning in the division of Intelligent Systems
Science at ISIR (Institute of Scientiﬁc and Industrial Research) of Osaka University since1996 until 2006. Further before joining the university, he had been with Hitachi since
1967, participated in research on nuclear reactor core management, control and design of
nuclear power reactors, expert systems for nuclear power plant diagnosis at the Central
Research Laboratory (1967-1971), the Atomic Energy Research laboratory (1971-1978)
and the Energy Research Laboratory (1978-1985), and on artiﬁcial intelligence, machinelearning, knowledge acquisition, qualitative reasoning and diagrammatic reasoning at theAdvanced Research Laboratory (1985-1995). He continued to work on machine learningand knowledge acquisition, and has extended his research to scientiﬁc knowledge discovery and data mining at Osaka University. At AOARD he had worked on social network
analysis while funding basic research in Asian Countries and managing projects in computational intelligence. He was the principal investigator of the active mining project
funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology(MEXT) that involved about 20 universities and research institutes (2001-2004). He received his Bs, Ms and PhD degrees in nuclear engineering from the University of Tokyo.
He was on the board of trustee of the Japan Society of Software Science and Technology(JSSST), the Japanese Society for Artiﬁcial Intelligence (JSAI) and the Japanese Cognitive Science Society (JCSS), and on the scientiﬁc advisory board of Alberta Ingenuity
Center of Machine Learning. He was the chair of SIG-KBS and SIG-FAI of JSAI, thechair of the steering committee of Paciﬁc Asian Conference of Knowledge Discovery andData Mining, the chair of the steering committee of International Conference on Discovery
Science, the chair of the steering committee of Asian Conference on Machine Learning,and on the editorial board of JSAI, JCSS and Knowledge Acquisitionq (Academic Press),
IEEE Expert, Knowledge and Information Systems: An International Journal (Springer),
Advanced Engineering Informatics (Elsevier), International Journal of Human-Computer
Studies (Elsevier), Intelligent Data Analysis: An International Journal (IOS Press) andInternational Journal of Data Science and Analytics (Springer). He is now an honorary
member of the steering committee of Paciﬁc Rim International Conference of Artiﬁcial

1Intelligence, a life long member of the steering committee of Paciﬁc Asian Conference ofKnowledge Discovery and Data Mining, an honorary member of the steering committee of
Asian Conference on Machine Learning, an honorary member of the steering committee ofInternational Conference on Discovery Science, and a member of the steering committeeof IEEE International Conference on Data Science and Advanced Analytics. He received
the best paper awards twice from Atomic Energy Society of Japan (1977, 1984), fourtimes from JSAI (1989, 1992, 2000, 2018), the best research paper award for DSAA2014(2014), the outstanding achievement awards from JSAI (2000), the distinguished contribution award for PAKDD (2006), the outstanding contribution award from Web Intelligence
Consortium (2008), the distinguished contribution award for PRICAI (2014) and the distinguished contribution award for ACML (2018). He co-authored/co-edited four books
on feature selection/extraction/construction. His book “Fundamentals of Data Mining”
was awarded the 2007 Okawa Publishing Prize. He was a member of AESJ, ANS, AAAI,
IEEE, JCSS, IPSJ and is a member of JSAI (Fellow) and WIA (Fellow).

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1. Personal Data
• Retired - Current Titles:
Professor Emeritus, Osaka University
Guest Professor, ISIR, Osaka University• Contact information:
Email: motoda (at) sanken.osaka-u.ac.jp
URL: http://www.ar.sanken.osaka-u.ac.jp/˜motoda/motopreg.html• Work history:
Scientiﬁc Advisor, Asian Oﬃce of Aerospace Research and Development, Air
Force Oﬃce of Scientiﬁc Research, Air Force Research Laboratory, USA (May,2006 to April, 2018)
Professor, The Institute of Scientiﬁc and Industrial Research, Osaka Universtiy (January, 1996 to March, 2006))
Senior Chief Research Scientist, Advanced Research Laboratory, Hitachi, Ltd.
(August, 1994 to December, 1995)
Chief Research Scientist, Advanced Research Laboratory, Hitachi, Ltd. (April,
1987 to July, 1994)
Senior Research Scientist, Advanced Research Laboratory, Hitachi, Ltd.
(April, 1985 to March, 1987)
Senior Research Scientist, Energy Research Laboratory, Hitachi, Ltd. (April,
1978 to March, 1985)
Senior Research Scientist, Atomic Energy Research Laboratory, Hitachi, Ltd.
(August, 1976 to March, 1978)
Visiting Scholar, Department of Nuclear Engineering, Purdue University(September, 1972 to August, 1973)
Research Scientist, Atomic Energy Research Laboratory, Hitachi, Ltd. (April,
1971 to July, 1976)
Research Scientist, Central Research Laboratory, Hitachi, Ltd. (April, 1967
to March, 1971)
2. Education• Teknowledge Inc. (Oct. 1981 - Mar. 1982)• Dept. of Nuclear Engineering, Purdue University, Visiting Scholar (Sep. 1972 Aug. 1973)• Department of Nuclear Engineering, Graduate School of Engineering, University ofTokyo (1965-1967)• Department of Nuclear Engineering, Faculty of Engineering, University of Tokyo(1963-1965)• College of General Education, University of Tokyo (1961-1963)
3. Degrees
PhD from department of Nuclear Engineering, University of Tokyo, 11/19723• Thesis: Optimization of in-core fuel management for nuclear power reactors
Ms from department of Nuclear Engineering, University of Tokyo, 03/1967• Thesis: Optimal control of nuclear reactor for xenon poisoning problem
Bs from department of Nuclear Engineering, University of Tokyo, 03/1965• Thesis: Experimental study of two-region coupled core in a sub-critical assembly
4. Awards
Fellow of Web Intelligence Academy, 10/2023
Special achievement award of the Paciﬁc Asian Conference on Data Mining and Knowledge Discovery, 05/2020
Best paper award of Japanese Society for Artiﬁcial Intelligence, 06/2019• Extracting Cooperative Structure among Online Items from Share-Event Sequence
Distinguished contribution award of the Asian Conference on Machine Learning,
11/2018
Best paper award runner up for the 14th Paciﬁc Rim International Conference on
Artiﬁcial Intelligence, 08/2016• Detecting Critical Links in Complex Network to Maintain Information Flow/Reachability
Best paper award for the special interest group of Japanese Society for Artiﬁcial Intelligence, 06/2016• Combining Activity-evaluation Information with NMF for Trust-link Prediction in
Social MediaDistinguished paper award for the annual conference of Japanese Society for Artiﬁcial
Intelligence, 06/2015• Identifying High Centrality Nodes in Social Network based on Gap Analysis witha Conﬁdence Level
Distinguished contribution award of the Paciﬁc Rim International Conference on Artiﬁcial Intelligence, 12/2014
Best research paper award for the 2014 International Conference on Data Science andAdvanced Analytics, 10/2014• Eﬃcient Analysis of Node Inﬂuence Based on SIR Model over Huge Complex Networks
Fellow of Japanese Society of Artiﬁcial Intelligence, 6/20094

Outstanding contribution award of Web Intelligence Consortium, 12/2008
Okawa publishing award, 11/2007• Fundamentals of Data Mining, Ohmsha, Ltd., 12/2006
Distinguished contribution award of the Paciﬁc Asian Conference on Data Mining andKnowledge Discovery, 04/2006
Best paper award for the special interest group of Japanese Society for Artiﬁcial Intelligence, 06/2005• Development of Classiﬁer by Using QAR Analysis
Best paper award for the special interest group of Japanese Society for Artiﬁcial Intelligence, 06/2003• A Data Mining System: MUSASHI
Distinguished paper award for the annual conference of Japanese Society for Artiﬁcial
Intelligence, 06/2003• Improvement of Search Capability of Decision Tree – Graph-Based Induction
Best paper award of Journal of Computer Aided Chemistry, 06/2002• Applying the Apriori-based Graph Mining Method to Mutagenesis Data Analysis,
Best paper award of Japanese Society for Artiﬁcial Intelligence, 05/2001• Scientiﬁc Law Discovery based on Sacale Type Constraint
Outstanding achievement award of Japanese Society for Artiﬁcial Intelligence, 06/2000
Distinguished paper award for the annual conference of Japanese Society for Artiﬁcial
Intelligence, 12/1999• Scientiﬁc Law Discovery from Observed Data and its Application to Socio-psychology
Distinguished paper award for the annual conference of Japanese Society for Artiﬁcial
Intelligence, 12/1998• The extended SDS: A Model of Scientiﬁc Discovery for Simultaneous Equation
Systems
Best paper award of the special interest geoup of Japanese Society for Artiﬁcial Intelligence, 12/1997• A method of discovering the ﬁrst principlce based on scale-based reasoning and its
implementation
Distinguished paper award for the annual conference of Japanese Society for Artiﬁcial
Intelligence, 06/1997• A data-driven method to discover the ﬁrst principle of complex systems
Best paper award of Japanese Society for Artiﬁcial Intelligence, 07/1993
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• Concept Learning from Inference Pattern
Distinguished paper award for the annual conference of Japanese Society for Artiﬁcial
Intelligence, 06/1991• Concept Learning from Inference Pattern
Best paper award of Japanese Society for Artiﬁcial Intelligence, 07/1990• Frustration-Based Learning in Auxiliary-Line Problems in Elementary Geometry
Best paper award of Atomic Energy Society of Japan, 03/1984• Method for plant operation guidance by knowledge engineering technique
Best paper award of Atomic Energy Society of Japan, 03/1977• Optimization of refueling schedule for light water reactors
Encouragement award for young scientists of Atomic Energy Society of Japan, 03/1970• Burnup optimization of nuclear power reactor
5. Membership of Academic Society• The Institute of Electrical and Electronics Engineering (Computer Society) from1990 to 2007
• Japanese Society of Artiﬁcial intelligence since 1987 (Fellow, 2009)• Japan Society of Software Science and Technology from 1986 to 2008
• Cognitive Science Society of Japan from 1986 to 2008
• American Association for Artiﬁcial Intelligence since 1985 to 2010
• Information Processing Society of Japan from 1982 to 2008
• Atomic Energy Society of Japan from 1965 to 1991
• American Nuclear Society from 1972 to 1990
6. Research Funds (since 1995 at Osaka University)
1. Japanese Ministry of Education, Culture, Sports, Science and Technology• Feature construction for structured data

2004-2005

13,800K yen• Data mining for inter-nanosience

2002-2005

27,400K yen• Active mining from structured data

2001-2004

67,610K yen• Implementation of active mining environ- 2001-2004
ment• Knowledge acquisition system for unstable 2001-2003
environmental• Structure activity mining of chemical com- 2000-2002
pounds38,400K yen

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13,600K yen
3,000K yen• Integrated learning environment for data 1999-2001
mining• Constructive induction of meaningful fea- 1999-2000tures• Knowledge discovery from large databases
1998-2000• User-adaptive interface that learns user’s 1997-1999
preference• Incremental development of consistent 1997-1998
knowledge bases

8,700K yen
2,100K yen
11,500K yen
12,300K yen
1,900K yen

2. Research Funds from AOARD (since 1995 at Osaka University)• Improvement of Apriori-based Graph Mining 2002-2003
Method• Integrated Knowledge Acquisition from both 2003-2004
human expert and accumulated data

2,000K yen
2,000K yen

3. Industry• 20041,000K yen• 2003780K yen• 2002500K yen• 20011,500K yen• 20001,300K yen• 1999800K yen• 19981,900K yen• 19973,600K yen

4. Research Experience
05/2018 Present

Professor Emeritus, Osaka University,
Guest Professor, the Institute of Scientiﬁc and Industrial Research,
Osaka University
Adjunct Professor, School of Computing and Information Systems,
University of Tasmania (03/2013 - 08/2018)• Social network analysis (Social dynamics, Critical link identiﬁcation, Change detection, Social bias, Social trust, etc.)• Continuing Data Mining and Machine Learning Research• Working as a collaborator for overseas projects in the area of computational intelligence7

05/2006 04/2018

Scientiﬁc Advisor
Asian Oﬃce of Areospace Research and Development
Air Force Oﬃce of Scientiﬁc Research
US Air Force Research Laboratory
7-23-17 Roppongi, Minato-ku, Tokyo 106-0032, Japan
Professor Emeritus, Osaka University,
Guest Professor, the Institute of Scientiﬁc and Industrial Research,
Osaka University
Visiting Professor, School of Computing, The University of New
South Wales (03/2008 - 02/2012)
Adjunct Professor, School of Computing and Information Systems,
University of Tasmania (03/2013 - 08/2018)• Funding and Managing projects as an international program oﬃcer in the area ofcomputational intelligence in Asian countries, in particular in the area of machinelearning, data mining and artiﬁcial intelligence.
• Continuing Data Mining and Machine Learning Research• Social network analysis (Information diﬀusion, Opinion formation, Extaction of
inﬂuential nodes, etc.)
01/1996 03/2006

Professor
Division of Intelligent Systems Science
Osaka University
8-1 Mihogaoka, Ibaraki, Osaka 567, Japan• Derivation of association rules for data with numerical attributes using densitybased clustering without explicit discretization• Discovery of time dependent law equations from numerical data by scale type tracking method• Implementation of active mining framework and its application to medical dataand chemical compound data
• Feature constriction by meta-learning• Uniﬁed approach for information retrieval with various data formats (both text andimage) based on invariant mathematical transformation using sliding windows ofbit sequences• Integration of incremental Knowledge acquisition from human expert and incremental machine learning from data in unstable environment within the frameworkof Ripple Down Rule method• Active knowledge acquisition by generating new cases in Ripple Down Rule method• Induction of decision tree for graph structured data with feature construction bygraph-based induction (DT-GBI, DT-ClGBI)
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• Eﬃcient approximate method of extracting typical patterns from graph structured
data by graph based induction (GBI) and its improvement (B-GBI, Cl-GBI)• Complete search for extracting frequent patterns from graph structured data by
Apriori-based graph mining (AGM)• Discovery of ﬁrst principle equations from numerical data
• Discretization of numeric data by Akaike’s information criterion (AIC) and minimum description length (MDL)• Adaptive user Interface that improves operability by inducing user’s behavioral
characteristics• Reduction of association rules by maximum inference using Apriori algorithm
08/1994 12/1995

Senior Chief Research Scientist and Research Group Leader
Advanced Research Laboratory
Hitachi, Ltd.
Hatoyama, Saitama, Japan• Information ﬁltering by probabilistic clustering method• Feasibility study of user adaptive interface
09/1987 07/1994

Chief Research Scientist and Research Group Leader
Advanced Research Laboratory
Hitachi, Ltd.
Hatoyama, Saitama, Japan• Large memory personal workstation (Learning machine as a life long partner)• Automatic conﬁguration of hidden Markov modeling by iterative link deletion algorithm and application to DNA classiﬁcation problem.
• Learning search control knowledge by perceptual chunking (visual pattern) andapplication to solving elementary geometry problems• Integration of deductive and inductive learning by typical pattern extraction• Eﬀective use of diagrams in visual reasoning (diagrammatic reasoning) and veriﬁcation by protocol analysis• Compositional modeling by explicitly stating the relevancy claims and meta-reasoning
over these claims (i.e. relevance reasoning)• Interpretation of a metaphorical sentence by representing verbs using a limited setof primitives and taking analogical mapping from which to learn a new meaning ofverbs.
• Development of a meta-interview system, a shell to generate a task-dependentinterview system which elicits knowledge by detecting deﬁciency while solving a
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problem.
• Knowledge compilation based on hierarchical approximations and abstractions
04/1985 08/1987

Senior Research Scientist and Research Group Leader
Advanced Research Laboratory
Hitachi, Ltd.

• Hierarchical qualitative reasoning and its application to radio circuit (an attempt
to device understanding by functional reasoning)• Validity proof of deﬁnite clause based on inductionless induction
04/1982 03/1985

Senior Researcher and Research Unit Leader
Energy Research Laboratory
Hitachi, Ltd.
Hitachi, Ibaraki• Expert system for boiling water reactor (BWR) power plant operator guidance bymodel based reasoning• Knowledge representation and inference mechanism for expert system shell basedon predicate calculus and meta-level reasoning• Inference mechanism for real time control and application to start up control of a
nuclear power plant• Expert system for a transformer plant layout by combining case-based reasoning
and heuristic reasoning.
10/1981 03/1982

Teknowledge, Inc.
Palo Alto, CA, USA• An architecture of expert system for BWR operator guidance
08/1980 09/1981

Senior Researcher
Energy Research Laboratory
Hitachi, Ltd.

• Strategic planning for research and development
04/1978 07/1980

Senior Researcher and Research Unit Leader
Energy Research Laboratory
Hitachi, Ltd.

08/1976 03/1978

Senior Researcher and Research Unit Leader
Atomic Energy Research Laboratory
Hitachi, Ltd.
Ozenji, Kawasaki
10• Improvement of core performance of existing BWR by backﬁtting• On-line monitoring system of core performance of BWR by use of nuclear-thermalhydraulic coupled model and in-core monitoring measurement• Three dimensional neutronics model for transient analysis of BWR by an improved
quasi static approximation• Veriﬁcation of in-core fuel management system for BWR• Operator guidance for load following of BWR
09/1973 07/1976

Researcher
Atomic Energy Research Laboratory
Hitachi, Ltd.

• On-line prediction system of core performance for BWR• Feasibility study of management information system for BWR• Program systems for in-core fuel management of BWR
09/1972 -Dea
08/1973

Visiting Scholar
Department of Nuclear Engineering
Purdue University, Indiana, USA• Optimization of in core fuel management of BWR
04/1971 08/1972

Researcher
Atomic Energy Research Laboratory
Hitachi, Ltd.

04/1967 03/1971

Researcher
Central Research Laboratory
Hitachi, Ltd.
Kokubunji, Tokyo• Theory of optimal control rod programming for in-core fuel management of BWR• Conceptual Design of Advanced Thermal Reactor• Cascade theory of uranium enrichment system using continuous stage model approximation
5. Academic activities
1. Advisory Board• 04/2003 - 10/2018

Technical Committee of Web Intelligence Consortium• 04/2003 - 05/2008

Scientiﬁc advisory board of Alberta Ingenuity Centre for
Machine Learning11• 04/2003 - 03/2006

Academic advisory board of Japan Institute of Scienceand Technology• 04/2004 - 03/2006

Academic advisory board of the Institute of Statistical
Mathematics

2. Board of Trustee• 01/2005 - 12/2006

Board of Auditor of Japanese Cognitive Science Society• 06/1995 - 05/1999

Board of trustee of Japanese Cognitive Science Society• 06/1990 - 05/1992

Board of trustee of Japanese Society for Artiﬁcial Intelligence• 05/1987 - 04/1989

Board of trustee of Japan Society of Software Scienceand Technology

3. Editorial Board• 04/2015 - 07/2022

Editorial board of International Journal of Data Scienceand Analytics (Springer)• 10/2009 - 03/2012

Advisory board of ACM Transactions on Intelligent Systems and Technology (ACM TIST)• 08/2002 - 12/2005

Editorial board of the AI Handbook (Japanese Societyof Artiﬁcial Intelligence)• 04/2001 - 05/2003

Advisory board of the Handbook of Data Mining(Lawrence Erlbaum Associates )• 04/2001 - 03/2014

Editorial board of Intelligent Data Analysis: An Internartional Journal (IOS Press)• 01/1998 - 12/2004

Editorial board of Knowledge and Information Systems:
An International Journal (Springer Verlag)• 03/1997 - 12/2002

Editorial board of the Handbook of Knowledge Discovery and Data Mining (Oxford University Press)• 11/1994 - 03/2006

Editorial board of International Journal of Human Computer Studies (Elsevier)• 06/1993 - 04/1997

Editorial board of Japanese Cognitive Science Society• 08/1991 - 05/2005

Editorial board of Advanced Engineering Informatics(Elsevier)• 01/1991 - 01/1993

Editorial board of Computer Science (Shujunsha)• 02/1990 - 02/1994

Editorial board of IEEE Expert• 04/1989 - 11/1994

Editorial board of the Journal of Knowledge Acquisition(Academic Press)• 04/1987 - 03/1991

Editorial board of Japanese Society for Artiﬁcial Intelligence12

4. Steering Committee Members• 11/2018 - Present

Discovery Science Conference, Honorary member• 08/2014 - 04/2018

International Conference on Informatics for Human
Brain, Behavior and Health• 12/2013 - Present

International Conference on Data Science and Advanced
Analytics• 05/2012 - Present• 10/2009 - 05/2012

Asian Conference on Machine Learning, Honorary member
Asian Conference on Machine Learning, Chair• 11/2006 - 11/2018

Discovery Science Conference• 05/2006 - Present

Paciﬁc-Asia Conference on Knowledge Discovery &Data Mining, Life long member• 04/2003 - 10/2018

WI (Web Intelligence) & IAT (Intelligent Agent Technology)• 11/2002 - 10/2006

Discovery Science Conference, Chair• 11/2002 - 10/2006

Algorithmic Learning Theory Conference• 05/2003 - 04/2006

Paciﬁc-Asia Conference on Knowledge Discovery &Data Mining, Chair• 05/2001 - 04/2003

Paciﬁc-Asia Conference on Knowledge Discovery &Data Mining, Co-chiar• 04/1998 - 04/2001

Paciﬁc-Asia Conference on Knowledge Discovery &Data Mining• 09/2004 - Present

Paciﬁc Rim International Conference on Artiﬁcial Intelligence, Honorary member• 08/1996 - 08/2004

Paciﬁc Rim International Conference on Artiﬁcial Intelligence

5. Program Chairs and Organizers• Honorary Co-Chair of the 2023 Principle and Practice of Data and Knowqledge
Acquisition Workshop: PKAW2023 (2023)• Honorary Co-Chair of the 2022 Principle and Practice of Data and Knowqledge
Acquisition Workshop: PKAW2022 (2022)• Honorary Co-Chair of the 2020 Principle and Practice of Data and Knowqledge
Acquisition Workshop: PKAW2020 (2021)• Honorary Conference Co-Chair of the Twelveth Asian Conference on Machine
Learning: ACML2020 (2020)• Honorary Co-Chair of the 2019 Paciﬁc Rim Knowqledge Acquisition Workshop:
PKAW2019 (2019)13• Honorary Conference Co-chair of the 22nd Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD19 (2019)• Honorary Conference Co-Chair of the Tenth Asian Conference on Machine
Learning: ACML2018 (2018)• Honorary Co-Chair of the 2018 Paciﬁc Rim Knowqledge Acquisition Workshop:
PKAW2018 (2018)• General Co-Chair of the 2017 IEEE International Confernece on Data Scienceand Advanced Analytics: DSAA2017 (2017)• General Co-Chair of the Third Asian Conference on Defense Technology: ACDT2017(2017)• Honorary Conference Co-Chair of the Eleventh International Conference on
Knowledge, Information and Creativity Support Systems: KICSS16 (2016)• Honorary Conference Co-Chair of the 14th Paciﬁc Rim International Conference on Artiﬁcial Intelligence: PRICAI2016 (2016)• Honorary Co-Chair of the 2016 Paciﬁc Rim Knowqledge Acquisition Workshop:
PKAW2016 (2016)• General Co-Chair of the Second Asian Conference on Defense Technology:
ACDT2016 (2016)• Honorary Conference Co-Chair of the Tenth International Conference on Knowledge, Information and Creativity Support Systems: KICSS15 (2015)• Conference Co-chair of the First Asian Conference on Defence Technology:
ACDT15 (2015)• Conference Co-chair of the Nineteenth Paciﬁc-Asia Conference on Knowledge
Discovery & Data Mining: PAKDD15 (2015)• Conference Co-Chair of the 2014 International Conference on Data Science andAdvanced Analytics (DSAA 2014)• Program Co-chair of the 2nd International Conference on Rough Sets and Intelligent Systems Paradigms (RSEISP 2014)• Honorary Conference chair of the Eighteenth Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD14 (2014)• Conference Co-Chair of the Ninth International Conference on Advanced Data
Mining and Applications (ADMA 2013)• Co-organizer of the Fourth International Workshop on Behavior and Social
Informatics and Computing: BSIC2013 in conjunction with IJCAI2013 (2013)• Co-organizer of the Fourth International Workshop on Behavior and Social
Informatics: BI2013 in conjunction with PAKDD2013 (2013)• Conference Co-Chair of the 17th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining: PAKDD2013 (2013)14

• Co-organizer of the Third International Workshop on Behavior Informatics:
BI2012 in conjunction with WI-IAT2012 (2012)• Honary Co-Chair of The 12th International Workshop on Knowledge Management and Acquisition for Intelligent Systems: PKAW2012 (2012)• Co-organizer of the Second International Workshop on Behavior Informatics:
BI2011 in conjunction with PAKDD2011 (2011)• Conference Chair of the Fifth International Conference on Knowledge, Information and Creativity Support Systems: KICSS 2010 (2010)• Best Paper Award Chair of the European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases: ECML/PKDD2010(2010)• Honary Co-Chair of The 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services: PKAW2010 (2010)• Co-organizer of the First International Workshop on Behavior Informatics:
BI2010 in conjunction with PAKDD2010 (2010)• Co-organizer of the Fourth International Workshop on Feature Selection andData Mining: FSDM2010 (2010)• Conference chair of the First Asisan Conference on Machine Learning (ACML09)• Honorary Conference chair of the Thirteenth Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD09 (2009)• Conference chair of the Tenth Paciﬁc Rim International Conference on Artiﬁcial
Intelligence (PRICAI2008)• Honorary Conference chair of the Eleventh Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD07 (2007)• Conference chair of the Eighth International Conference on Discovery Science(DS2005).
• Conference Chair of the Ninth Paciﬁc-Asia Conference on Knowledge Discovery& Data Mining: PAKDD05 (2005)• Co-organizer of the International Workshop on Active Mining: AM-2004, 2004
• Honoyrary Chair of the Fifth Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW04, 2004
• Co-organizer of the International Workshop on Active Mining: AM-2003, 2003
• Organizer of the International Workshop on Active Mining: AM-2002, 2002
• Co-organizer of the Fourth Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW02, 2002
• Co-organizer of the First International Workshop on Data Mining Lessons
Learned (DMLL-2002), Workshop at ICML2002, Sydney, 8-12 July, 2002
• Organizer of the panel “Innovative Projects for Intelligent Systems in the New
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Century” for 13th International Symposium on Methodologies for Intelligent
Systems: ISMIS02, 2002
• Co-organizer of the Third Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW00, 2000
• Co-Organizer of the Fourth Paciﬁc-Asia Conference on Knowledge Discovery& Data Mining: PAKDD2000, 2000
• Program chair of the First International Workshop on Discovery Science: DS98,
1998• Co-organizer of the Second Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW98, 1998
• Co-program chair of the Fifth Paciﬁc Rim International Conference on Artiﬁcial
Intelligence: PRICAI98, 1998
• Co-program chair of the First Paciﬁc-Asia Conference on Knowledge Discovery& Data Mining: PAKDD97, 1997
• Co-organizer of the First Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW96, 1996
• Co-organizer of the Third Japanese Knowledge Acquisition for KnowledgeBased Systems Workshop: JKAW94, 1994
• Co-organizer of International Workshop on Machine Intelligence 1995: MI95,
1993• Co-organizer of International Workshop on Machine Intelligence 1993: MI93,
1993• Co-organizer of the Second Japanese Knowledge Acquisition for KnowledgeBased Systems Workshop: JKAW92, 1992
• Co-organizer of the First Japanese Knowledge Acquisition for Knowledge-Based
Systems Workshop: JKAW90, 1990
• Vice Chairman of the 5th Annual Meeting of Japanese Society for Artiﬁcial
Intelligence, 1990
6. Program Committee• Technical Committee of The 3rd International Conference on Machine Learning
and Soft Computing (ICMLSC2019)• Program Committee of the 6th International Workshop on New Frontiers in
Mining Complex Patterns (NFMCP2017)• Program Committee of the 23rd International Symposium on Methodologies
for Intelligence Systems (ISMIS2017)• Program Committee of the 14th International Workshop on Knowledge Management and Acquisition for Intelligent Systems (PKAW2016)16

• Program Committee of the 6th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2015)• Program Committee of International Conference on Social Computing, BehavioralCultural Modeling, and Prediction (SBP2013)• Program Committee of the 12th International Workshop on Knowledge Management and Acquisition for Intelligent Systems (PKAW2012)• Program Committee of International Conference on Social Computing, BehavioralCultural Modeling, and Prediction (SBP2012)• Program Committee of IJCAI2011 Workshop on Link Mining in Heterogeenous
Information Networks (HINA2011)• Program Committee of International Conference on Social Computing, BehavioralCultural Modeling, and Prediction (SBP2011)• Program Committee of the 19th International Symposium on Methodologies
for Intelligent Systems (ISMIS2011)• Program Committee of the 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services (PKAW2010)• Program Committee of the Third IEEE/ACM International Conference on Cyber,Physical and Social Computing (CPSCom2010)• Program Committee of the Thirteenth International Conference on Discovery
Science (DS2010)• Program Committee of the 2010 International Conference on Social Computing,
Behavioral Modeling, and Prediction (SBP2010)• Program Committee of the Twelfth International Conference on Discovery Science (DS2009)• Program Committee of the 2009 IEEE International on Social Computing, Services and Intelligence (SocialCom-09) (2009)• Program Committee of the Second International Workshop on Social Computing, Behavioral Modeling, and Prediction, Phoenix, Arizona (2009)• Program Committee of the 2008 Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW08 (2008)• Program Committee of the International workshop on New challenges for Feature Selection in Data Mining and Knowledge Discovery (FSDM08)• Program Committee of Brazilian Symposium on Artiﬁcial Intelligence (SBIA
2008)• Program Committee of the First International Workshop on Social Computing,
Behavioral Modeling, and Prediction, Sedona, Arizona (2008)• Program Committee (Area chair) of the 12th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’08) (2008)17

• Program Committee of the 18th European Conference on Machine Learling(ECML) and the 11th European Conference on Principles and Practice ofKnowledge Discovery in Databases (PKDD) (2007)• Program Committee of the 11th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’07) (2007)• Program Committee of the Third Workshop on Mining Complex Data (MCD)at the IEEE International Conference on Data Mining (ICDM’07) (2007)• Program Committee of the Second Workshop on Mining Complex Data (MCD)at the IEEE International Conference on Data Mining (ICDM’06) (2006)• Program Committee of the Sixth IEEE International Conference on Data Mining (ICDM’06) (2006)• Program Committee (Seniro member) of the 19th Australian Joint Conferenceon Artiﬁcial Intelligence (AI2006) (2006)• Program committee of the 16th International Symposium on Methodologies forIntelligent Systems ISMIS06 (2006)• Program Committee of International Workshop on Feature Selection for Data
Mining Interfacing Machine Learning with Statistics: FSDM2006 (2006)• Senior Program Committee the 23rd International Conference on Machine
Learning (ICML-2006) (2006)• Program Committee of the Ninth Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD06 (2006)• Program Committee of the workshop on Foundation of Semantic Oriented Dataand Web Mining (2005)• Program Committee of the 4th Workshop on Multi-Relational Data Mining(MRDM-2005)• Program Committee of the 15th International Conference on Inductive Logic
Programmingthe (ILP2005)• Program Committee of the Fifth IEEE International Conference on Data Mining (ICDM2005)• Program Committee of the Eleventh ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining (KDD2005)• Program Committee of the 10th International Conference on Rough Sets, Fuzzy
Sets, Data Mining, and Granular Computing (RSFDGrC2005)• Program Committee of the 16th European Conference on Machine Learling
ECML (Area chair) (2005)• Program Committee of the 9th European Conference on Principles and Practiceof Knowledge Discovery in Databases PKDD (Area chair) (2005)• Program Committee (Area chair) of the 22nd International Conference on Machine Learning (ICML-2005) (2005)18

• Program Committee for the Symposium on Abstraction, Reformulation andApproximation in 2005 (SARA 2005)• Program Committee of the Ninth Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD05 (2005)• Program Committee of International Workshop on Feature Selection for Data
Mining Interfacing Machine Learning with Statistics: FSDM2005 (2005)• Program Committee of the Fourth IEEE International Conference of Data Mining (ICDM 2004)• Program Committee for Foundation of Data Mining Workshop, colocated workshop of ICDM-2004, November, Brighton, UK.
• Program Committee of the Second International Workshop on Mining Graphs,
Trees and Sequences (MGTS-2004), 15th European Conference on Machine
Learning (ECML’04) and 8th European Conference on Principles and Practiceof Knowledge Discovery in Databases• Program Committee for Multi Relational Data Mining (MRDM-2004), colocated workshop of SIGKDD-2004, Workshop at SIGKDD2004, August, Seattle,
USA.
• Program Committee of International Workshop on Knowledge Discovery in
BioMedicine (KDbM-04).
• Program Committee of BASYS’04, 6th IFIP International Conference on Information Technology for BALANCED AUTOMATION SYSTEMS in Manufacturing and Services, Track D: Machine learning and data mining in industry• Program Committee of 14th International Conference on Knowledge Engineering and Management - EKAW 2004
• Program Committee of the 15th European Conference on Machine Learling
ECML• Program Committee of the 8th European Conference on Principles and Practiceof Knowledge Discovery in Databases PKDD
• Program Committee of the Seventh International Conference on Discovery Science (DS2004).
• Program Committee of the Sixteenth European Conference on Artiﬁcial Intelligence, ECAI2004, 2004
• Program Committee of the Fifth Paciﬁc Rim Knowledge Acquisition for KnowledgeBased Systems Workshop: PKAW04, 2004
• Program Committee of the Eighth Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD04 (2004)• Program Committee of the First International Workshop on Mining Graphs,
Trees and Sequences (MGTS-2003), 14th European Conference on Machine
Learning (ECML’03) and 7th European Conference on Principles and Practiceof Knowledge Discovery in Databases
19• Program Committee of the Sixth International Conference on Discovery Science(DS2003).
• Program Committee for Multi Relational Data Mining (MRDM-2003), colocated workshop of KDD-2003, Workshop at KDD2003, August, Washington
DC, USA.
• Program Committee of the 2003 International Workshop on Data Mining for
Software Engineering and Knowledge Engineering (DMSK’03)• Program Committee for the Third IEEE International Conference on Data
Mining (ICDM ’03)
• Program Committee of the 14th European Conference on Machine Learning(ECML-2003)• Program Committee of the 7th European Conference on Principles and Practiceof Knowledge Discovery in Databases (PKDD-2003)• Program Committee of the Fourteenth International Symposium for Methodology of Intelligent Systems: ISMIS2003, 2003
• Program Committee of the International Workshop on Foundation of Data
Mining and Discovery, 2003
• Programm Committee of the Seventh Paciﬁc-Asia Conference on Knowledge
Discovery & Data Mining: PAKDD03, 2003
• Program Committee for Multi Relational Data Mining (MRDM-2002), colocated workshop of KDD-2002, Workshop at KDD2002, July 23 - 26, 2002,
Edmonton, Alberta, Canada• Program Committee of the 6th European Conference on Principles and Practiceof Knowledge Discovery in Databases (PKDD’02), August 19-23, 2002, Helsinki• Program Committee of the 13th European Conference on Machine Learning(ECML’02), August 19-23, 2002, Helsinki• Program Committee of ICIIP’02, the International Conference on Intelligent
Information Processing, 22-25 September 2002, Beijing• Program Committee of ICML’02, the Nineteenth International Conference on
Machine Learning (ICML-2002), 8-12 July 2002, The University of New South
Wales, Sydney, Australia• Program Committee of the Fifth International Conference on Discovery Science(DS2002), December 2002
• Program Committee of EKAW’02, the 13th European International Conference on Knowledge Engineering and Knowledge Management, Madrid (Spain),
October 1-4 , 2002.
• Program Committee of the Symposium on Abstraction, Reformulation andApproximation in 2002 (SARA-2002), Kananaskis, Alberta, Canada• Programm Committee of the Sixth Paciﬁc-Asia Conference on Knowledge Dis20

covery & Data Mining: PAKDD02, 2002
• Program Committee of the IJCAI-2001 Workshop on Wrappers for Performance
Enhancement in Knowledge Discovery in Databases (KDD), 04 Aug 2001, Seattle, Washington, USA• Program Committee of the Fourth International Conference on Discovery Science (DS2001), November 2001.
• International Committee of the Third International Workshop on Strategic
Knowledge and Concept Formation (SKCF’01) to be held in Sydney, 17-19
December 2001.
• Program Committee of the 12th European Conference on Machine Learning(ECML’01), 2001
• Program Committee of the 5th European Conference on Principles and Practiceof Knowledge Discovery in Databases (PKDD’01), 2001
• Program Committee of the First IEEE International Conference on Data Mining (ICDM ’01), 2001
• Programm Committee of the Fifth Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD01, 2001
• Advisory Board of the Second Asia-Paciﬁc Conference on Intelligent Agent
Technology (IAT’2001), 2001
• Program Committee of the Third International Conference on Discovery Science (DS2000), 2000
• Program Committee (area chair) of the Seventeenth International Conference
on Machine Learning, 2000
• Program Committee of the 12th International Conference on Knowledge Engineering and Knowledge Management (EKAW2000), 2000
• Program Committee of the Symposium on Abstraction, Reformulation andApproximation, (SARA2000), 2000
• Program Committee of the Sixth International Workshop on Multistrategy
Learning, 2000
• Programm Committee of the Second Workshop on Strategic Knowledge andConcept Formation, 1999
• Program Committee of the Second International Conference on Discovery Science (DS1999), 1999
• Program Committee of Australian Applications of AI Symposium, December
1999• Program Committee of the IJCAI99 workshop on Learning about Users, Stockholm, Sweden, July, 1999
• Advisory Committee of the Second International Conference on Cognitive Science Tokyo, Japan July 27-30, 199921

• Program Committee of The Seventh International Workshop on Rough Sets,
Fuzzy Sets, Data Mining, and Granular-Soft Computing (RSFDGrC’99), November 9-11, 1999 in Yamaguchi Resort Center, Ube, Yamaguchi, Japan.
• Programm Committee of the Third Paciﬁc-Asia Conference on Knowledge Discovery & Data Mining: PAKDD99, 1999
• Program Committee of International Symposium on Intelligent Data Engineering and Learning (IDEAL) to be held in Hong Kong, October 14-16, 1998
• Program Committee of the Sixth International Workshop on Rough Sets, Data
Mining and Granular Computing, 1998
• Program Committee of the Fourth International Workshop on Multistrategy
Learning, Desenzano del Garda, Italy, 1998
• Program Committee of Symposium on Abstraction, Reformulation and Approximation: SARA-98, 1998
• Program Committee of The Ninth Workshop on Algorithmic Learning Theory:
ALT98, 1998
• Program Committee of The Australian Joint Conference on Artiﬁcial Intelligence (AI98), 1998
• Programm Committee of the Second Paciﬁc-Asia Conference on Knowledge
Discovery & Data Mining: PAKDD98, 1998
• Programm Committee of the 1997 IEEE Knowledge and Data Engineering
Exchange Workshop (KDEX-97), 1997
• Programm Committee of the Workshop on Strategic Knowledge and Concept
Formation, 1997
• Program committee of the Eleventh Banﬀ Knowledge Acquisition for KnowledgeBased Systems Workshop: KAW97, 1997
• Program committee of the Tenth European Knowledge Acquisition for KnowledgeBased Systems Workshop: EKAW97, 1997
• Programm Committee of the Workshop on Problem-solving Methods for Knowledgebased Systems in Connection with the Fifteenth International Joint Conferenceon Artiﬁcial Intelligence (IJCAI-97), 1997
• Program Committee of The Australian Joint Conference on Artiﬁcial Intelligence (AI97), 1997
• Program Committee of The Eighth Workshop on Algorithmic Learning Theory:
ALT97, 1997
• Program Committee of The Joint Paciﬁc Asian Conference on Expert Systems/Singapore International Conference on Intelligent Systems (PACES/SPICIS97),
1997• Program Committee of The Fourth Paciﬁc Rim International Conference on
artiﬁcial Intelligence (PRICAI96), 199622

• Program committee of the Ninth European Knowledge Acquisition for KnowledgeBased Systems Workshop: EKAW96, 1996
• Program committee of the Tenth Banﬀ Knowledge Acquisition for KnowledgeBased Systems Workshop: KAW96, 1996
• Program Committee of The Thirteenth National Conference on Artiﬁcial Intelligence (AAAI96), 1996
• Program Committee of The Fourth International Workshop on Rough Sets,
Fuzzy Sets and Machine Discovery (RSFM’96), 1996
• Program Committee of The Workshop on Machine Learning Meets Human
Computer Interaction, 1996
• Program Committee of The Third World Congress on Expert Systems, 1996¡• Program Committee of Symposium on Abstraction, Reformulation, and Approximations (SARA-95), 1995
• Program Committee of Paciﬁc-Asian Conference on Expert Systems (PACES95),
1995• Program committee of the Ninth Banﬀ Knowledge Acquisition for KnowledgeBased Systems Workshop: KAW95, 1995
• Program Committee of The Fifth International Conference on Human-MachineInteraction and Artiﬁcial Intelligence in Aerospace (HMI-AI-AS’95), 1995
• Program Committee of The Eighth International Conference on Industrial &Engineering Applications of Artiﬁcial Intelligence & Expert Systems, 1995
• Program Committee of The Fifth Workshop on Algorithmic Learning Theory:
ALT94, 1994
• Program Committee of The Eleventh International Conference on Machine
Learning: ICML94, 1994
• Program and organizing committee of the Second Japan/Korea Joint Conference on Expert Systems: JKJCES94, 1994
• Program committee of the Eighth European Knowledge Acquisition for KnowledgeBased Systems Workshop: EKAW94, 1994
• Program committee of the Eighth Banﬀ Knowledge Acquisition for KnowledgeBased Systems Workshop: KAW94, 1994
• Program Committee of The Fourth Workshop on Algorithmic Learning Theory:
ALT93, 1993
• Program committee of the Seventh European Knowledge Acquisition for KnowledgeBased Systems Workshop: EKAW93, 1993
• Program committee of the First Korea/Japan Joint Conference on Expert Systems: KJJCES93, 1993
• Program Committee of The Third Workshop on Algorithmic Learning Theory:23

ALT92, 1992
• Program Committee of the Second Paciﬁc Rim International Conference on
Artiﬁcial Intelligence: PRICAI92, 1992
• Program committee of the Seventh Banﬀ Knowledge Acquisition for KnowledgeBased Systems Workshop: KAW92, 1992
• Program committee of the Sixth European Knowledge Acquisition for KnowledgeBased Systems Workshop: EKAW92, 1992
• Program Committee of The Second International Conference on Fifth Generation Computer Systems: FGCS92, 1992
• Program Committee of The Second Workshop on Algorithmic Learning Theory:
ALT91, 1991
• Program committee of the Sixth Banﬀ Knowledge Acquisition for KnowledgeBased Systems Workshop: KAW91, 1991
• Program committee of the Fifth European Knowledge Acquisition for KnowledgeBased Systems Workshop: EKAW91, 1991
• Program Committee of International Conference on Knowledge Modeling andExpertise Transfer: KMET91, 1991
• Program Committee of the First Paciﬁc Rim International Conference on Artiﬁcial Intelligence: PRICAI90, 1990
• Local Arrangements Committee of First International Workshop on Algorithmic Learning Theory: ALT90, 1990
• Program Committee of the 6th Conference of Japan Society of Software Science,
1989
7. SIGs and Working Groups• 04/1993 - 08/1995

Working group of Curriculum Systematization of
Knowledge Information Processing Engineering for University Education• 04/1990 - present

SIG-KBS (Knowledge Based Systems) of Japanese Society for Artiﬁcial Intelligence, Chair from 04/1994 to
03/1996• 04/1990 - present

SIG-FAI (Fundamental AI) of Japanese Society for Artiﬁcial Intelligence, Secretary from 4/1990 to 3/1992,
Chair from 4/1998 to 3/2000• 01/1989 - 04/1996

Working group on AI application to nuclear plant ofJapan Atomic Energy Institute• 04/1988 - 03/1989

ICOT working group on knowledge base system architecture
ICOT working group on knowledge acquisition for design problem• 04/1988 - 03/198924

• 04/1985 - 03/1990

ICOT working group on fundamental AI• 04/1985 - 03/1990

ICOT working group on expert system of power generating plant• 04/1984 - 03/1991

SIG for artiﬁcial intelligence and knowledge engineeringof The Institute of Electronics, Information and Communication Engineers• 04/1984 - 03/1990

SIG for knowledge engineering and artiﬁcial intelligenceof Information Processing Society of Japan• 04/1971 - 10/1976

SIG for nuclear system technology of Atomic Energy
Society of Japan• 04/1969 - 03/1971

SIG for optimization technology of nuclear system ofAtomic Energy Society of Japan

8. External Thesis Reviewer• 037/2007 - 05/2007

PhD Thesis reviewer of Megan Margaret Vazey, Macquarie University• 07/2006 - 05/2006

PhD Thesis reviewer of Richard Peter Dazeley, University of Tasmania• 12/2005 - 03/2006

PhD Thesis reviewer of Le Hoang Minh, Japan Advanced Institute of Science and Technology• 12/2005 - 03/2006

PhD Thesis reviewer of Ngyen Dung Duc, Japan Advanced Institute of Science and Technology• 06/2005 - 09/2005

PhD Thesis reviewer of Le Si Quang, Japan Advanced
Institute of Science and Technology• 06/2003 - 09/2003

PhD Thesis reviewer of Saori Kawasaki, Japan Advanced Institute of Science and Technology• 05/2003 - 08/2003

PhD Thesis reviewer of Kanh Hyuk Lee, University ofSydney• 05/2001 - 08/2001

PhD Thesis reviewer of Ashesh Mahidadia, Universityof New South WalesPhD Thesis reviewer of Nguyen Dung Trong, Japan Advanced Institute of Science and Technology• 10/2000 - 03/2001• 03/2000 - 06/2000• 05/1998 - 08/1998

PhD Thesis reviewer of Ghassan Beydoun, University of
New South WalesPhD Thesis reviewer of Debbie Richards, University of
New South Wales

9. Invited Talks at Majour Conferences and Workshops• “Learning Information Diﬀusion Models from Observation and Its Application
to Behavior Analysis”, The 3rd International Conference on Social Informatics(2011)
25

• “What can We Discover from Graph-Structured Data? - A data mining perspective -”, The 20th International Conference on Industrial, Engineering &Other Applications of Applied Intelligent Systems (2007)• “What can we do with graph-structured data? - A data mining perspective -”,The Ninteenth Australian Joint Conference on Artiﬁcial Intelligence (2006)• “Active Mining, A Spiral Model of Knowledge Discovery from Data”, The FirstWorld Congress of the International Federation for Systems Research (2005)• “Active Mining - A Spiral Model of Knowledge Discovery from Data”, The
Ninteenth Fuzzy System Symposium of the Japan Society for Fuzzy THEory
and Intelligent Informatics (2003)• “Active Mining, A Spiral Model of Knowledge Discovery”, The 2002 IEEE
International Conference on Data Mining (2002)• “Feature Selection, Extraction and Construction”, Foundation of Data Mining,
PAKDD02 (2002)• “Active Mining - Scope and Perspective”，AI Seminar at the Sixteenth Annual
Meeting of JSAI (2002)• “Acquiring Knowledge from both Human and Data under Unstable Environment”, Tenth Portuguse Conference on Artiﬁcial Intelligence (EPIA-01) (2001)• “Mining Patterns from Graph-Structured Data”, Fifth International Workshop
on Multistrategy Learning (MSL-00) (2000)• “Mining Frequent Patterns from Graph-Structured Data”, Twelveth Australian
Joint Conference on Artiﬁcial Intelligence (AI-99) (1999)• “Computer Assisted Discovery of First Principle Equations”, Third Paciﬁc-Asia
Conference on Knowledge Discovery and Data Mining (PAKDD-99) (1999)• “Machine Learning Techiniques to Make Computers Easier to Use”, FifteenthInternational Conference on Artiﬁcial Intelligence (IJCAI-97) (1997)• “Can Machine Learn in The Same Way as Humans Do?” First International
Conference on Applied Ergonomics (1997)
10. Publication lists
1. Journals[1] T. Fushimi, K. Saito, K. Ohara, M. Kimura and H. Motoda, “Constructing
Outlier-free Histograms with Variable Bin-Width based on Distance Minimization,”Intelligent Data Analysis, An International Journal, Vol.27, No.1,
pp.5-29 (2023) pdf[2] T. Fushimi, K. Saito and H. Motoda, “Eﬃcient Computation of Expected
Motif Frequency in Uncertain Graphsby Exploiting Possible World Marginalization and Motif Transition,” Social Network Analysis and Mining, Springer,
Vol.12, Issue 1, Article no.126 (2022) pdf

26

[3] T. Fushimi, K. Saito, K. Ohara, M. Kimura and H. Motoda, “General Framework of Opening and Closing Shops over a Social Network based on Stochastic
Utility under Competitive and Time-bounded Environment,”Social Network
Analysis and Mining, Springer, Vol.11, Issue 1, Article no.70 (2021) pdf[4] K. Saito, T. Fushimi, K. Ohara, M. Kimura and H. Motoda, “Eﬃcient computation of target-oriented link criticalness centrality in uncertain graphs,”Intelligent
Data Analysis, An International Journal, Vol.25, No.5, pp.1323-1343 (2021)
pdf[5] K. Ohara, K. Saito, M. Kimura and H. Motoda, “Critical Node Identiﬁcation
based on Articulation Point Detection for Uncertain Network,” International
Journal of Networking and Computing, Vol.9, No.2, pp.201-216 (2019) pdf[6] K. Ohara, K. Saito, M. Kimura and H. Motoda, “Resampling-based predictive
simulation framework of stochastic diﬀusion model for identifying top-K inﬂuential nodes,” International Journal of Data Science and Analytics, Springer,
Vol.9, No.2, pp.175-195 (2020) pdf[7] K. Saito, K. Ohara, M. Kimura and H. Motoda, “Accurate and Eﬃcient Detection of Critical Links in Network to Minimize Information Loss,” Journalof Intelligent Information Systems, Springer, Vol.51, No.2, pp.235-255 (2018)
pdf[8] K. Matsutani, M. Kumano, M. Kimura, K. Saito, K. Phara and H. Motoda,“Extracting Cooperative Structure among Online Items from Share-Event Sequence,” (in Japanese) Vol.33, No.3A, pp.1-12 (2018) pdf[9] K. Saito, K. Ohara, M. Kimura and H. Motoda, “Which is More Inﬂuential,“Who” or “When” for a User to Rate in Online Review Site?,” Intelligent
Data Analysis, An Intelligent Data Analysis, An International Journal, Vol.22,
No.3, pp.639-657 (2018) pdf[10] M. Kimura, K. Saito, K. Ohara and H. Motoda, “Speeding-up node inﬂuence computation for huge social networks,” International Journal of Data
Scienceand Analytics, Springer, Vol.1, No.1, pp. 3-15 (2016) pdf[11] K. Hatta, M. Kumano, M. Kimura, K. Saito. K. Ohara and H. Motoda,”Analysis of Mediator-Activity Eﬀects for Trust-Network Evolution in Social
Media,” Trans. Mathematical Modeling and Its Applications, IPSJ, Vol.8,
No.2, pp.44-56 (2015) pdf[12] K. Saito, M. Kimura, K. Ohara and H. Motoda, ”Super mediator – A newcentrality measure of node importance for information diﬀusion over social
network Reference,” Journal of Information Sciences, Elsevier, 329, pp.9851000 (2016), Available online 23 Mar. 2015 pdf[13] K. Saito, K. Ohara, M. Kimura and H. Motoda,”Change Point Detection for
Burst Analysis from an Observed Information Diﬀusion Sequence of Tweets”,
Journal of Intelligent Information Systems, Springer, Vol. 44, pp. 243-269(2015) pdf[14] K. Saito, M. Kimura, K. Ohara, and H. Motoda, “ Learning Asynchronous27

Time Information Diﬀusion Models and its Application to Behavioral Data
Analysis over Social Networks,” Journal of Computer Engineering and Informatics (JCEI), Vol. 1, Iss. 2, pp. 30-57 (2013) pdf[15] M. Kimura, K. Saito, K. Ohara and H. Motoda, “Learning to predict opinion share and detect anti-majority opinionists in social networks,”Journal ofIntelligent Information Systems, Springer, Vol.41, pp.5-37 (2013) pdf[16] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Detecting Changes in Information Diﬀusion Pattern over Social Network,” ACM Transactions on Intelligent Systems and Technology (TIST), Vol.4, Iss. 3, pp.55:1-55:23 (2013)pdf[17] K. Ohara, K. Saito, K. Kimura and H. Motoda, “Burst Detection in a Sequence of Tweets based on Information Diﬀusion Model,” Journal of the Data
Base Society of Japan, Vol.11, No.2, pp.25-30 (2012) pdf[18] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Eﬃcient Discovery of Inﬂuential Nodes for SIS Models in Social Networks,” Knowledge and Information
Systems: An International Journal, Vol.30, No.3, pp.613-635 (2012) pdf[19] Yuya Yoshikawa, Kazumi Saito, Hiroshi Motoda, Kouzou Ohara, Masahiro
Kimura,”Estimating Method of Expected Inﬂuence Curve from Single Diﬀusion Sequence on Social Networks,”The IEICE Transactions (Japanese Edition), Vol.J94-D,No.11, pp.1899-1908 (2011 pdf[20] M. Kimura, K. Saito, K. Ohara and H. Motoda, “Learning Information Diﬀusion Model in a Social Network for Predicting Inﬂuence of Nodes,” Intelligent
Data Analysis, An International Journal, Vol.15, No.4, pp.633-652 (2011) pdf[21] M. Kimura, K. Saito, R. Nakano and H. Motoda, “Extracting Inﬂuential
Nodes on a Social Network for Information Diﬀusion,” Data Mining andKnowledge Discovery,” Springer, Vol.20, No.1, pp.70-97 (2010) pdf[22] M. Kimura, K. Saito, R. Nakano and H. Motoda, ”Learning Information Diﬀusion Model for Extracting Inﬂuential Nodes in a Social Network,” in Japanese,
Transaction of JSAI, Vol.25, No.1, pp.215-223 (2010) pdf[23] H. Motoda, “Problem with Research on Artiﬁcial Intelligence (in Japanese)”,Manufacturing and Technology, Vol.61, No.3, pp.23-26 (2009) pdf[24] M. Kimura, K. Saito and H. Motoda, “Blocking Links to Minimize Contamination Spread in a Social Network,”ACM Transactions on Knowledge Discovery
from Data, Vol. 3, No.2: pp.9:1-9.23 (2009) pdf[25] A. Termier, M. Rousset, M. Sebag, K. Ohara, T. Washio and H. Motoda,“DryadeParent: An Eﬃcient and Robust Closed Attribute Tree Mining Algorithm”, IEEE Transaction on Knowledge and Data Engineering, Vol.20, No.3,
pp.300-320 (2008) pdf[26] Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang,
Hiroshi Motoda, Geoﬀrey J. McLachlan, Angus Ng, Bing Liu, Philip S.Yu,
Zhi-Hua Zhou, Michael Steinbach, David J.Hand, Dan Steinberg, “Top 10

28

Algorithms in DataMining,” Knowledge and Information Systems, An International Journal, Vol.14, No. 1, pp.1-37 (2007) pdf[27] H. Motoda, “Pattern Discovery from Graph-Structured Data - A Data Mining
Perspective,” in New Trends in Applied Artiﬁcial Intelligence, LNAI4570, pp.
12-22 (2007) pdf[28] T. Washio, K. Nakanishi and H. Motoda, “A Classiﬁcation Method Based on
Subspace Clustering and Association Rules”, New Generation Compututing,
Vol.25, No.3, pp.235-245 (2007)[29] T. Washio, T. Higuchi, S. Imoto, Y. Tamada, K. Sato and H. Motoda, “Graph
Mining and Its Aplication to Statistical Modeling”, Proc. of the Institute of
Statistical Mathematics, Vol.54, No.2, PP.315-331 (2006) pdf[30] T. Wakaki, H. Ikeda, M. Tamura, H. Motoda and T. Washio, “A Study on
Rough Set-aided Feature Selection for Automatic Web-Page Classiﬁcation”,
Web Intelligence and Agent Systems: An International Journal, Vol.4, No.4,
pp.431-441 (2006) pdf[31] K. Nakanishi, T. Washio, Y. Mitsunaga, A. Fujimoto and H. Motoda, “AClassiﬁcation Method Based on Subspace Clustering and Association Rules(in Japanese)”, Transaction of JSAI, Vol.21, No.6, pp.526-536 (2006) pdf[32] Y. Misunaga, T. Washio and H. Motoda, “Mining Quantitative Frequent Itemsets using Adaptive Density-based Subspace Clustering (in Japanese)”, Transaction of JSAI, Vol.21, No.5, pp.439-449 (2006) pdf[33] K. Yada, H. Motoda, T. Washio and A. Miyawaki, “Consumer Behavior Analysis by Graph Mining Technique”, New Mathematics and Natural Computation, Vol.2, No.1, pp.59-68 (2005) pdf[34] F. Adachi, T. Washio and H. Motoda: “Scientiﬁc Discovery of Dynamic Models Based on Scale-type Constraints,, IPSJ Transactions on Mathematical
Modeling and Its Applications, Vol.47, No.SIG14(TOM15), pp.31-42 (2006)
pdf[35] F. Adachi, T. Washio, A. Fujimoto, H. Motoda and H. Hanafusa, “Development of Generic Search Method Based on Transformation Invariance”, New
Generation Conputing Journal, Vol.23, No.4, pp.291-313 (2005) pdf[36] W. Geamsakul, T. Yoshida, K. Ohara, H. Motoda, H. Yokoi and K. Takabayashi,“ Constructing a Decision Tree for Graph-Structured Data and its
Applications”, Fundamenta Informaticae, Vol.66, No.1-2, pp.131-160 (2005)
pdf[37] A. Inokuchi, T. Washio and H. Motoda, “General Framework for Mining Frequent Subgraphs from Labeled Graphs”, Fundamenta Informaticae, Vol.66,
No.1-2, pp.53-82 (2005) pdf[38] T. Washio, H. Motoda and Y. Niwa, “Enhancing the plausibility of law equation discovery through cross check among multiple scale-type-based models”,
Journal of Experimental & Theoretical Artiﬁcial Intelligence, Vol. 17, No.

29

1-2, pp.129-143 (2005) pdf[39] S. Tsumoto, T. Yamaguchi, M. Numao and H. Motoda, ”On the special issueof ”Active Mining”, ”J. of JSAI, Vol.20, No.2, pp.145-148 (2005) pdf[40] H. Liu, H. Motoda and L. Yu, “A selective sampling approach to active feature
selection”, Artiﬁcial Intelligence, Vol 159/1-2 pp.49-74 (2004) pdf[41] N. Lavrac, H. Motoda, T. Fawcett, R. Holte, P. Langley and P. Adriaans,“Introduction, Lessons Learned from Data Mining Applications and Collaborative Problem Solving”, Machine Learning, Vol. 57, No.1-2, pp.13-34 (2004)pdf[42] N. Lavrac, H. Motoda, T. Fawcett, “Editorial:Data Mining Lessons Learned”,
Machine Learning, Vol. 57, No.1-2, pp.5-11 (2004) pdf[43] A. Inokuchi, T. Washio and H. Motoda, “Generalization for Frequent Subgraph Mining (in Japanese)”, Journal of JSAI, Vol.19, No.5, pp.368-378 (2004)pdf[44] T. Yoshida, T. Wada, H. Motoda and T. Washio, “Adaptive Ripple Down
Rules Method based on Description Length”, Transaction of JSAI Vol. 19,
No. 6-B, pp. 460-471 (2004) pdf[45] T. Yoshida, T. Wada, H. Motoda and T. Washio, “Adaptive Ripple Down
Rules Method Based on Minimum Description Length Principle”, Inteligent
Data Analysis, An International Journal, Vo. 8, No. 3, pp.239-265 (2004) pdf[46] K. Yoshida, F. Adachi, T. Washio, H. Motoda, T. Homma, A. Hakashima ,H. Fujikawa and K. Yamazaki, “Density-based Spam Detector”, IEICE Trans.
Vol.E87-D, No.12, pp.2678-2688 (2004) pdf[47] Y. Nishimura, T. Washio, T. Yoshida, H. Motoda, A. Inokuchi and T. Okada,“Fast Apriori-based Graph Mining ALgorithm and application to 3-dimensional
Structure Analysis (in Japanese)”, Transactions of JSAI, Vol.18, No.5 C,
pp.257-268 (2003) pdf[48] A. Inokuchi, T. Washio and H. Motoda, “Complete Mining of Frequent Patterns from Graphs: Mining Graph Data”, Machine Learning, Vol.50, No.3,
pp.321-354 (2003) pdf[49] S. Hori, H. Taki, T. Washio and H. Motoda, “Applying data mining to aﬁeld quality watchdog task”, Electrical Engineering in Japan, Vol.140, Iss. 2,
Wiley, pp.18-25, (2002) pdf[50] M. Terabe, T. Washio, H. Motoda, O. Katai and T. Sawaragi, “Attribute
Generation Based on Association Rues”, Knowledge and Information Systems,
An International Journal, Vol. 4, No. 3, pp.329-349 (2002) pdf[51] T. Matsuda, H. Motoda and T. Washio, “Graph-Based Induction and Its
Applications,” Advanced Engineering Informatics, Vol. 16, No. 2, pp.135-143(2002) pdf[52] H. Liu and H. Motoda, “On Issues of Instance Selection,” Data Mining andKnowledge Discovery, Vol. 6, No. 2, pp.115-130 (2002) pdf
30[53] T. Washio and H. Motoda, “Toward the Discovery of First Prnciple Based
Scientiﬁc Law Equations,” Progress in Discovery Science (Final Report of
the Japanese Discovery Science Project), S. Arikawa and A. Shinohara Eds.,
Lecture Note in Artiﬁcial Intelligence, LNAI 2281, Springer Verlag, pp.553-564(2002) pdf[54] A. Inokuchi, T. Washio, T. Okada and H. Motoda, “Applying the Aprioribased Graph Mining Method to Mutagenesis Data Analysis”, J. of Computer
Aided Chemistry, Vol. 2, pp.87-92 (2001) pdf[55] S. Hori, H. Taki, T. Washio and H. Motoda, “Applying Data Mining to Field
Quality Watchdog Task, T. IEEE Japan, Vol.121-C, No.8, pp.1289-1295 (2001)
pdf[56] M. Terabe, T. Washio and H. Motoda, “Fast Classiﬁer Generation by S3bagging”, Trans. Mathematical Modeling and Its Applications, IPSJ, Vol.
42, No. 14, pp.25-38 (2001) pdf[57] T. Wada, T. Horiuchi, H. Motoda and T. Washio, “A Description LengthBased Decision Criterion for Default Knowledge in the Ripple Down Rules
Method”, Knowledge and Information Systems, Springer, Vol.3, No.2, pp.146167 (2001) pdf[58] T. Matsuda, H. Motoda, T. Washio, “Graph-Based Induction for General
Graph Structured Data and Its Applications,” Trans. of JSAI, Vol.16, No.4,
A, pp.363-374 (2001) pdf[59] T. Wada, H. Motoda and T. Washio, “Integrating Inductive Learning to the
Ripple Down Rules Method with the Minimum Description Length Principle,
Trans. of JSAI, Vol. 16, No. 2, pp.268-278 (2001) pdf[60] T. Washio, H. Motoda, “Discovery of Scientiﬁc Simultaneous Equation Modelsof Large Scale Systems,” J. of JSAI, Vol.15, No.6, pp.1107-1116 (2000) pdf[61] A. Inokuchi, T. Washio, H. Motoda, K. Kumazawa and N. Arai “Fast andComplete Mining Method for Frequent Graph Patterns,” J. of JSAI, Vol.15,
No.6, pp.1052-1063 (2000) pdf[62] T. Washio, H. Motoda, “Discovery of Scientiﬁc Law Equations Based on ScaleType Constraints,” J. of JSAI, Vol.15, No.4, pp.681-692 (2000) pdf[63] T. Washio and H. Motoda, “Extension of Association Rule Mining for Structured and Numerical Data ”, J. of JSAI, Vol.15, No.5, pp.759-767 (2000) pdf[64] T. Kayama, T. Horiuchi, H. Motoda and T. Washio, “Classiﬁcation Rule
Learning from Tree Structured Data by Stepwise Pair Expansion”, J. of JSAI,
Vol.15, No. 3, pp.485-494 (2000) pdf[65] T. Wada, T. Horiuchi, H. Motoda and T. Washio, “Decision Criterion of
Default Knowledge Based on Characterization of Knowledge Acquisition in
Ripple Down Rules Methods, ” Journal of JSAI, Vo.15, No. 1, pp.177-186(2000) pdf[66] M. Terabe, O. Katai, T. Sawaragi, T. Washio and H. Motoda, “Attribute31Generation based on Association Rules (in Japanese)”, Journal of JSAI, Vo.15,
No. 1, pp.187-197 (2000) pdf[67] H. Motoda and K. Yoshida, “Machine Learning Techniques to Make Computers Easier to Use, Journal of Artiﬁcial Intelligence, Vol. 103 No.1-2, pp.295-321(1998) pdf[68] T. Washio and H. Motoda, “Compositional Law Discovery Based on Scale
Cognition of Feature Quantities (in Japanese),” Cognitive Studies: Bulletinof the Japanese Cognitive Science Society, Vol.5，No.2，pp.80-94 (1998) pdf[69] T. Washio and H. Motoda, “Discovery of First Principle Equations Basedon Scale-Type-Based and Data-Driven Reasoning, Knowledge-Based Systems,
Elsevier, Vol.10, No.7, pp.403-411 (1998) pdf[70] H. Liu and H. Motoda, “Feature Transformaion and Subset Selection,” IEEE
Intelligent Systems, Vol. 13, No. 2, March/April, pp.26-28 (1998) pdf[71] B.H. Kang, K. Yoshida, H. Motoda and P. Compton, “Help Desk Systemwith Intelligent Interface”, Applied Artiﬁcial Intelligence: An International
Journal, Vol.11, pp.611-631 (1997) pdf[72] K. Yoshida and H. Motoda, “Inductive Inference by Stepwise Pair Extension,”in Japanese, Journal of JSAI, Vol.12, No.1, pp.58-67 (1997) pdf[73] K. Yoshida and H. Motoda, “Automated User Modeling for Intelligent Interface, ” International Journal of Human Computer Interaction (invited), Vol.
8, No. 3, pp.237-258 (1996) pdf[74] M. Suwa and H. Motoda, “Operator Schemata Acquisition based on Recognition Propagation Rule, ” Cognitive Studies: Bulletin of the Japanese Cognitive
Science Society, Vol.2，No.4，pp.39-55 (1995) pdf[75] M. Suwa and H. Motoda, “On Dealing with the Dynamic Utility of Learned
Knowledge,” Machine Intelligence, Vol.14, pp.111-130, Oxford Univ. Press(1995) pdf[76] K. Yoshida and H. Motoda, “CLIP: Concept Learning from Inference Pattern,” Joural of Artiﬁcial Intelligence, Vol.75, No.1, pp.63-92 (1995) pdf[77] K. Yoshida, H. Motoda and N. Indurkhya, “Towards a Common Framework
for Inductive and Deductive Learning using Colored Digraphs,” in Japanese,
Journal of JSAI, Vol.10, No.1, pp.61-71 (1995) pdf[78] M. Suwa and H. Motoda, “PCLEARN: A Computer Model for Learning Perceptual Chunks,” AI communication, Vol.7, No.2, pp.114-125 (1994) pdf[79] K. Yoshida, H. Motoda and N. Indurkhya, “Graph-based Induction as a Uniﬁed Learning Framework”, Invited, J. of Applied Intelligence, Vol.4, .297-328(1994) pdf[80] M. Suwa and H. Motoda, “Perceptual Chunk Learning – Visual Search Control
Method –,” in Japanese, J. of JSAI, Vol.9, No.4, pp.548-558 (1994) pdf[81] M. Suwa and H. Motoda, “Primitive Matching – A Learning Method for
32

Metaphorical Relations –,” in Japanese, J. of JSAI, Vol.9, No.3, pp.417-426(1994) pdf[82] M. Suwa and H. Motoda, “Learning Perceputually-chunked Macro-operators,”
Machine Intelligence, Vol.13, pp.419-440, Oxford Univ. Press (1994) pdf[83] K. Yoshida and H. Motoda, “Hierarchical Knowledge Representation basedon Approximations,” International Journal of Expert Systems, Vol.5, No.2,
pp.105-119 (1993) pdf[84] H. Motoda, R. Mizoguchi and T. Nishdia, “Workshop on Knowledge Sharing
and Reuse”, Invited, J. of JSAI, Vol.8, No.5, pp.666-671 (1993) pdf[85] K. Yoshida and H. Motoda,“CLIP: Concept Learning from Inference Pattern(2) - Concept Hierarchy Formation -,” in Japanese, J. of JSAI, Vol.7, No.4,
pp.686-696 (1992) pdf[86] K.Yoshida and H. Motoda, “CLIP that Learns Concepts from Inference Process (in Japanese)”, Invited, Fall Issue of Nikkei Intelligent Systems, pp.152159 (1992)[87] H. Motoda, “Large Knowledge Bases for Engineering (translation in Japanese),”Invited, Computer Science, Vol.2, No.1, pp.66-75 (1992)[88] K. Yoshida and H. Motoda,“CLIP: Concept Learning from Inference Pattern(1) - A Method to Find Typical Inference Pattern -,” in Japanese, J. of JSAI,
Vol.7, No.4, pp.675-685 (1992) pdf[89] K. Yoshida and H. Motoda,“Hierarchical Knowledge Representation based on
Approximations,” in Japanese, J. of JSAI, Vol.7, No.1, pp.69-76 (1992) pdf[90] A. Kawaguchi, H. Motoda and R. Mizoguchi, “Interview-Based Knowledge
Acquisition using Dynamic Analysis,”, Invited, IEEE Expert, Vol.6, No.5,
pp.47-60 (1991) pdf[91] H. Motoda and A. Kawaguchi, “The Computer as a Life-Long Partner,” inJapanese, Invited, Computer Science, Vol.1, No.2, pp.107-113 (1991) pdf[92] M. Suwa and H. Motoda, “Understanding Metaphors by Frustration-Based
Learning Method,” in Japanese, J. of JSAI, Vol.5, No.3, pp.291-299 (1990)
pdf[93] K. Yoshida and H. Motoda, “An Approach to Hierarchical Qualitative Reasoning – Constructing Shallow Knowledge from Deep Knowledge,” in Japanese,J. of JSAI, Vol.4, No.4, pp.447-455 (1989) pdf[94] M. Suwa and H. Motoda, “Frustration-Based Learning in Auxiliary- Line
Problems in Elementary Geometry,” in Japanese, J. of JSAI, Vol.4, No.3,
pp.308-320 (1989) pdf[95] M. Suwa and H. Motoda, “Acquisition of Associative Knowledge by the FrustrationBased Learning Method in an Auxiliary-Line Problem,” Knowledge Acquisition, Vol.1, No.1, pp.113-137 (1989) pdf[96] N. Yamada and H. Motoda, “A Plant Diagnosis Method Based on the Knowl33

edge of System Description,” J. Inf. Proc. Vol.7, No.3, pp.143-148 (1984)pdf[97] H. Motoda and N. Yamada, “Nuclear Power Plant Diagnosis based on the
Functional Knowledge,” in Japanese, Measurement and Control, Vol.22, No.9,
pp.47-52 (1983) pdf[98] T. Kiguchi, K. Yoshida, H. Motoda and S. Kobayashi, “Method for plant
operation guidance by knowledge engineering technique,” in Japanese, Journalof Atomic Energy Society of Japan, Vol.25, No.4, 298-305 (1983) pdf[99] Y. Nishizawa, H. Motoda, N. Yamada and Y. Wada, “Approach to Knowledge
Based Man-Machine Communication for BWR Start-Up Guidance,” J. Nucl.
Sci. Technol., Vol.20, No.10, pp.877-879 (1983) pdf[100] K. Umegaki, T. Kiguchi and H. Motoda, “Simple Method to Predict PowerLevel and Core Flow Rate of Boiling Water Reactors by Using a One-Point
Core Model,” J. Nucl. Sci. Technol. Vol.19, No.7, pp.513-520 (1982) pdf[101] Y. Bessho, H. Motoda and M. Watanabe, “Principles for Control Rod Withdrawal Strategy during the Startup of Boiling Water Reactors,” Nucl. Technol., Vol.58, pp.113-119 (1982) pdf[102] H. Motoda, T. Hayase, Y. Bessho and K. Kato, “Multi-Region Neutronics
Model Based on Coarse Mesh Nodal Coupling Method for Slow Transient
Analyses of Boiling Water Reactors,” Nucl. Sci. Eng., Vol.80, pp.648-666(1982) pdf[103] Y. Bessho, H. Motoda, T. Kiguchi, T. Hayase, K. Hoshi and T. Enomoto,“A New Method of Startup Planning for Boiling Water Reactors ,” J. Nucl.
Sci. Technol. Vol.18, No.9, pp.697-704 (1981) pdf[104] S. Uchikawa and H. Motoda, “A Method for Calculating Power Distributions
in Boiling Water Reactors Using In-Core Detector Readings,” Nucl. Sci. Eng.,
Vol.77, pp.137-145 (1981) pdf[105] T. Hayase and H. Motoda, “Boiling Water Reactor Control Rod Programming Using Heuristic and Mathematical Methods,” Nucl. Technol., Vol.48,
pp.91-100 (1980) pdf[106] H. Motoda, S. Tanisaka, T. Kiguchi and H. Yonenaga, “Feasibility Study ofCore Management System by Data Communication for Boiling Water Reactors,” Nucl. Technol., Vol.36, pp.294-304 (1977) pdf[107] H. Motoda and O. Yokomizo, “Method to Minimize Power Peaking in Refueling Schedule of Boiling Water Reactor,” J. Nucl. Sci. Technol., Vol.14,
pp.108-116 (1977) pdf[108] Y. Niki, N. Hiranuma, M. Ozawa, H. Motoda and A. Kawahara, “Operating Experience of Shimane Nuclear Power Station and Core Management
Engineering System,” Hitachi Review, Vol.25, pp.97-102 (1976) pdf[109] O. Yokomizo, H. Motoda, T. Kiguchi and R. Takeda, “A Man-Machine Communication System for Boiling Water Reactor Core Management Planning,”
34

Nucl. Technol., Vol.29, pp.191-199 (1976) pdf[110] T. Kawai, H. Motoda, T. Kiguchi and M. Ozawa, “A Method for Generatinga Control Rod Programming for Boiling Water Reactors,” Nucl. Technol.,Vol.28, pp.108-118 (1976) pdf[111] Y. Nishizawa, T. Kiguchi and H. Motoda, “On-Line Prediction of the PowerDistribution within Boiling Water Reactors,” Nucl. Sci. Eng., Vol.60, pp.189192 (1976) pdf[112] H. Motoda and O. Yokomizo, “Optimization of Fuel Assembly Allocationfor Boiling Water Reactors,” J. Nucl. Sci. Technol., Vol.13, No.5, pp.230-246(1976) pdf[113] H. Motoda, J. Herczeg and A. Sesonske, “Optimization of Refueling Schedulefor Light Water Reactors,” Nucl. Technol., Vol.25, pp.477-496 (1975) pdf[114] T. Kiguchi, H. Motoda and T. Kawai, “Stochastic Fluctuation in a UraniumEnriching Cascade Using the Centrifuge Process,” Nucl. Technol., Vol.17,
pp.168-183 (1973) pdf[115] T. Kawai, K. Inoue, H. Motoda, T. Kobayashi and T. Kiguchi, “Sensitivity
Analysis of Ideal Centrifuge Cascade for Producing Slightly Enriched Uranium,” Nucl. Sci. Eng., Vol.50, pp.63-72 (1973) pdf[116] Y. Nishizawa and H. Motoda, “On-Line Calculation Method for Evaluating
the Power Distribution in a Nuclear Reactor Core,” J. Nucl. Sci. Technol.,Vol.10, No.2, pp.125-127 (1973) pdf[117] H. Motoda, “Investigation of the Fuel Loading Pattern on the Core Burnup
by FLARE Simulation,” J. Nucl. Sci. Technol., Vol.9, No.12, pp.751-753(1972) pdf[118] M. Kitamura and H. Motoda, “Burnup Optimization Using Modal Expansion
Method,” J. Nucl. Sci. Technol., Vol.9, pp.512-520 (1972) pdf[119] H. Motoda, “Optimization of Control Rod Programming and Loading Pattern in Multiregion Nuclear Reactor by the Method of Approximation Programming,” Nucl. Sci. Eng., Vol.49, pp.515-524 (1972) pdf[120] Y. Nishizawa and H. Motoda, “Method for Evaluating the Local Variationof the Neutron Flux,” J. Nucl. Sci. Technol., Vol.9, No.7, pp.412-419 (1972)pdf[121] H. Motoda, “Optimal Control Rod Programming of Light Water Reactors
in Equilibrium Fuel Cycle,” Nucl. Sci. Eng., Vol.46, pp.88-111 (1971) pdf[122] H. Motoda, “Burnup Optimization of Continuous Scattered Refueling,” Nucl.
Sci. Eng., Vol.41, pp.1-13 (1970) pdf[123] H. Motoda and T. Kawai, “A Theory of Control Rod Programming Optimization in Two-Region Reactors,” Nucl. Sci. Eng., Vol.39, pp.114-118 (1970)
pdf
2. Proceedings of conferences35[1] T. Fushimi, K. Saito, and H. Motoda, “Eﬃcient Analitical Computation of
Expected Frequency of Motifs of Small Size by Marginalization in Uncertain
Network,” Proceedings of the 2021 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining (ASONAM 2021), pp.1-8,
https://doi.org/10.1145/3487351.3488275 (2021) pdf[2] T. Fushimi, K. Saito, K. Ohara, M. Kimura, and H. Motoda, “Eﬃcient Computing of PageRank Scores on Exact Expected Transition Matrix of Large
Uncertain Graph,” Proceedings of the 2020 IEEE International Conference
on Big Data (IEEE BigData 2020), pp.916-923 (2020) pdf[3] T. Fushimi, K. Saito, K. Ohara, M. Kimura, and H. Motoda, “Opening andClosing Dynamics of Competing Shop Groups over Spatial Networks,” Proceedings of the 2020 IEEE/ACM International Conference on Advances in
Social Networks Analysis and Mining (ASONAM 2020), pp.393-400 (2020)
pdf[4] K. Ohara, T. Fushimi, K. Saito, M. Kimura, and H. Motoda, “Maximizing
Network Performance by Injecting Most Eﬀective k-links under the Presenceof Time Constraint,” Proceedings of The 23rd International Conference on
Discovery Science (DS2020), LNAI 12323, pp.421-436 (2020) pdf[5] K. Saito, K. Ohara,M. Kimura, and H. Motoda, “Remanpling-Based Framework for Unbiased Estimator of Node Centrality over Large Complex Network,” Proceedings of The 22nd International Conference on Discovery Science (DS2019), LNAI 11828, pp.428-429 (2019) pdf[6] K. Saito, K. Ohara,M. Kimura, and H. Motoda, “Eﬃcient Identiﬁcation of
Critical Links Based on Reachability under the Presence of Time Constraint,”
Proceedings of The 16th Paciﬁc Rim International Conference on Artiﬁcial
Intelligence (PRICAI2019), LNAI 11671, Springer, pp.404-418 (2019) pdf[7] K. Ohara, K. Saito, M. Kimura and H. Motoda, “Critical Node Identiﬁcation based on Articulation Point Detection for Network with Uncertain
Connectivity,” Proceedings of The Sixth International Symposium on Computing and Networking (CANDAR2018), IEEE Explore, DOI 10.1109/CANDAR.2018.00027, pp. 146-152 (2018) pdf[8] K. Saito, K. Ohara,M. Kimura, and H. Motoda, “Critical Link Identiﬁcation
based on Bridge Detection for Network with Uncertain Connectivity,” Proceedings of The 24th International Symposium on Methodologies of Intelligent
Systems (ISMIS2018), LNAI 11177, pp. 89-99 (2018) pdf[9] K. Saito, K. Ohara,M. Kimura, and H. Motoda, “Eﬃcient Detection of Critical
Links to Maintain Performance of Network with Uncertain Connectivity”,
Proceedings of The 15th Paciﬁc Rim International Conference on Artiﬁcial
Intelligence (PRICAI2018), LNAI 11012, Springer, pp.282-295 (2018) pdf[10] K. Matsutani, M. Kumano, M. Kimura, K. Saito, K. Ohara, and H. Motoda, “Discovering Cooperative Structure among Online Items for Attention
Dynamics,”Proceedings of Data science for human performance in social networks Workshop (ACUMEN’2017), in 2017 IEEE International Conference on
36

Data Mining Workshops, pp.1033-1041 (2017) pdf[11] K. Ohara, K. Saito, M. Kimura, and H. Motoda, “Maximizing Network Performance based on Group Centrality by Creating Most Eﬀective k-links,”Proceedingsof The 4th IEEE Data Science and Advanced Analytics (DSAA2017), IEEE
Explore, DOI 10.1109/DSAA.2017.44, pp.561-570 (2017) pdf[12] K. Saito, K. Ohara, M. Kimura and H. Motoda, “An Accurate and Eﬃcient
Method to Detect Critical Links to Maintain Information Flow in Network,”
Proceedings of the 23rd International Symposium on Methodologies for Intelligent Systems (ISMIS2017), LNCS 10352, pp.116-126 (2017) pdf[13] M. Choy, D. Kim, J. Lee, H. Kim and H. Motoda, “Looking Back on the
Current Day: Interruptibility Prediction Using Daily Behavioral Features”,
Proceedings of the 2016 AVM International Joint Conference on Pervasive
and Ubiquitous Computing (UbiComp2016), pp.1004-1015 (2016) pdf[14] K. Ohara, K. Saito, M. Kimura, and H. Motoda, “Accelerating computationof distance based centrality measures for spatial networks”, Proceedings ofthe 19th International Conference of Discovery Science, LNAI 9956, Springer,
pp.376-391 (2016) pdf[15] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Detecting Critical Links in
Complex Network to Maintain Information Flow/Reachability”, Proceedingsof The 14th Paciﬁc Rim International Conference on Artiﬁcial Intelligence(PRICAI2016), LNAI 9810, pp.419-432 (2016) pdf[16] K. Matsutani, K. Kumano, M. Kimura,K. K. Saito, Ohara and H. Motoda,“Combining Activity-evaluation Information with NMF for Trust-link Prediction in Social Media”, Fist International Workshop on Mining Big Data in
Social Networks (MBD-SONET) in conjunction with 2015 IEEE International
Big Data Conference, Proceedings of the 2015 IEEE International Conference
on Big Data (Big Data), pp.2101-2110 (2015) pdf[17] K. Ohara, K. Saito, M. Kimura, and H. Motoda, “Change point detection for
information diﬀusion tree”, Proceedings of the 18th International Conferenceof Discovery Science, LNAI 9356, Springer, pp.161-169 pdf[18] K. Ohara, K. Saito, M. Kimura, and H. Motoda, “Resampling-based Gap
Analysis for Detecting Nodes with High Centrality on Large Social Netework”,
Proceedings of the 19th Paciﬁc-Asia Conference on Knowledge discovery andData Mining, Part 1, LNAI 9077, pp.135-147 (2015) pdf[19] K. Saito, K. Ohara M. Kimura, and H. Motoda, “Eﬃcient Learning of User
Conformity on Review Score,” Proceedings of the 8th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction(SBP2015), LNCS 9021, pp.182-192 (2015) pdf[20] Y. Yamagishi, K. Saito, K. Ohara, M. Kimura and H. Motoda, “A Method
to Divide Stream Data of Scores over Review Sites,” Proceedings of The 13th
Paciﬁc Rim International Conference on Artiﬁcial Intelligence (PRICAI2014),
LNAI 8862, pp.913-919 (2014) pdf
37

[21] K. Hatta, M. Kumano, M. Kimura, K. Saito, K. Ohara, and H. Motoda,“Analyzing Mediator-Activity Eﬀects for Trust-Network Evolution in Social
Media,” Proceedings of The 13th Paciﬁc Rim International Conference on
Artiﬁcial Intelligence (PRICAI2014), LNAI 8862, pp.297-308 (2014) pdf[22] M. Kimura, K. Saito, K. Ohara, and H. Motoda, “Eﬃcient Analysis of NodeInﬂuence Based on SIR Model over Huge Complex Networks,” Proceedingsof The 2014 International Conference on Data Science and Advanced Analytics (DSAA2014), IEEE Xplore DOI: 10.1109/DSAA.2014.7058076, pp.216-222(2014) pdf[23] K. Ohara, K. Saito, M. Kimura, and H. Motoda, “Resampling-based Framework for Estimating Node Centrality of Large Social Network,” Proceedingsof The 17th International Conference on Discovery Science (DS2014), LNAI
8777, pp.228-239 (2014) pdf[24] K. Saito, M. Kimura, K. Ohara and H. Motoda, “A New Approach for Item
Ranking Based on Review Scores Reﬂecting Temporal Trust Factor,” Proceedings of the 7th International Conference on Social Computing, BehavioralCultural Modeling and Prediction (SBP2014), LNCS 8393, pp.161-168 (2014)pdf[25] K. Ohara, K. Saito, M. Kimura, and H. Motoda, “Predictive Simulation
Framework of Stochastic Diﬀusion Model for Identifying Top-K Inﬂuential
Nodes,” Proceedings of the 5th Asian Conference on Machine Learning, JMLR
Workshop and Conference Proceedings, Vol.29, pp.149-164 (2013) pdf[26] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Identifying Super-Mediatorsof Information Diﬀusion in Social Networks,” Proceedings of The 16th International Conference on Discovery Science (DS2013), LNAI 8140, pp.170-184(2013) pdf[27] K. Saito, K. Ohara, M. Kimura and H. Motoda, “Detecting Changes in Content and Posting Time Distributions in Social Media,” Proceedings of the2013 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, pp.572-578 (2013) pdf[28] K. Saito, M. Kimura, K. Ohara, H. Motoda, “Which Targets to Contact First
to Maximize Inﬂuence over Social Network, Proceedings of the 6th International Conference on Social Computing, Behavioral-Cultural Modeling andPrediction (SBP2013), LNCS 7812, pp.359-367 (2013) pdf[29] K. Saito, K. Ohara, M. Kimura and H. Motoda, “Burst Detection in a Sequence of Tweets based on Information Diﬀusion Model,” Proceeding of The
15th International Conference on Discovery Science (DS2012), LNAI 7569,
pp.239-253 (2012) pdf[30] M. Kimura, K. Saito, K. Ohara and H. Motoda, “Opinion Formation by Voter
Model with Temporal Decay Dynamics,” Proceeding of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
in Databases (ECML/PKDD2012), LNAI 7524, pp.565-580 (2012) pdf

38

[31] S. Kato, A. Koide, T. Fushimi, K. Saito and H. Motoda, “Network Analysisof Three Twitter Functions: Favorite, Follow and Mention,” Proceedings ofthe 12th International Worksop PKAW 2012: Knowledge Management andAcquisition for Intelligent Systems, LNAI 7457, pp.298-312 (2012) pdf[32] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Graph embedding on spheresand its application to visualization of information diﬀusion data,” Proceedingsof the International Workshop on Mining Social Network Dynamics (MSND2012),
pp.1137-1144 (2012) (http://www2012.org/proceedings/companion/p1137.pdf)
pdf[33] K. Ohara, K. Saito, M. Kimura and H. Motoda, “Eﬀect of In/Out-Degree
Correlation on Inﬂuence Degree of Two Contrasting Information Diﬀusion
Models,” Proceedings of the 5th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP2012), LNCS 7227,
pp.131-138 (2012) pdf[34] T. Fushimi, Y. Kubota, K. Saito, M. Kimura, K. Ohara and H. Motoda,“Speeding up Bipartite Graph Visualization Method,” Proceedings of the 24thAustralasian Joint Conference on Artiﬁcial Intelligence, LNAI-7106, pp.697706 (2011) pdf[35] Y. Yamagishi, K. Saito, K. Ohara, M. Kimura, and H. Motoda, “Learning
Attribute-weighted Voter Model over Social Networks,” Proceedings of the
3rd Asian Conference on Machine Learning, JMLR Workshop and Conference
Proceedings, Vol.20, pp.263-280 (2011) pdf[36] A. Koide, K. Saito, K. Ohara, M. Kimura, and H. Motoda, “Estimating Diﬀusion Probability Changes for AsIC-SIS Model from Information Diﬀusion Results,” Proceedings of the 3rd Asian Conference on Machine Learning, JMLR
Workshop and Conference Proceedings, Vol.20, pp.297-315 (2011) pdf[37] M. Kimura, K. Saito, K. Ohara and H. Motoda, “Detecting Anti-majority
Opinionists Using Value-weighted Mixture Voter Model,” Proceedings of the
14th International Conference on Discovery Science (DS2011), LNAI 6926,
pp.150-164 (2011) pdf[38] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Eﬃcient Detection of
Hot Span in Information Diﬀusion from Observation,” Proceedings of the
IJCAI Workshop on Link Analysis in Heterogeneous Information Networks(HINA2011), arXiv: 1110.2659 (2011) pdf[39] K. Saito, K. Ohara, Y. Yamagishi, M. Kimura and H. Motoda, “Learning Diffusion Probability based on Node Attributes in Social Networks,” Proceedingsof the 19th International Symposium on Methodologies for Intelligent Systems(ISMIS2011), LNAI 6804, pp.153-162 (2011) pdf[40] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Detecting Changes in Opinion Value Distribution for Voter Model,” Proceedings of the 4th International
Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP2011), LNAI 6389, pp.89-96 (2011) pdf

39[41] H. Liu, H. Motoda, R. Setiono, and Z. Zhao, “Feature selection: An ever
evolving frontier in data mining,” J. of Machine Learning Research, Vol. 10,
pp.4-13 (2010) pdf[42] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Discovery of Super-Mediatorsof Information Diﬀusion in Social Networks,” Proceedings of the 13th International Conference on Discovery Science (DS2010), LNAI 6332, pp.144-158(2010) pdf[43] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Generative Models of Information Diﬀusion with Asynchronous Time-delay,” Proceedings of the 2nd
Asian Conference on Machine Learning, JMLR Workshop and Conference
Proceedings, Vol.13, pp.193-208 (2010) pdf[44] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Selecting Information Diffusion Models over Social Networks for Behavioral Analysis,” Proceedings ofthe 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), LNAI 6323,
pp.180-195 (2010) pdf[45] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Eﬃcient Estimation of Cumulative Inﬂuence for Multiple Activation Information Diﬀusion Model with
Continuous Time Delay,” Proceedings of the 11th Paciﬁc Rim International
Conference on Artiﬁcial Intelligence (PRICAI2010), LNAI 6230, pp.244-255(2010) pdf[46] T. Fushimi, K. Saito, M. Kimura, H. Motoda and K. Ohara, “Finding Relation
between PageRank and Voter Model,” Proceedings of the 11th International
Worksop PKAW 2010: Knowledge Management and Acquisition for Smart
Systems and Services, LNAI 6232, pp.208-222 (2010) pdf[47] Y. Yoshikawa, K. Saito, H. Motoda, M. Kimura and K. Ohara, “Acquiring
Expected Inﬂuence Curve from Single Diﬀusion Sequence,” Proceedings ofthe 11th International Worksop PKAW 2010: Knowledge Management andAcquisition for Smart Systems and Services, LNAI 6232, pp.273-287 (2010)pdf[48] M. Kimura, K. Saito, K. Ohara and H. Motoda, “Learning to Predict Opinion
Share in Social Networks,” Proceedings of the Twenty-Fourth AAAI Conference on Artiﬁcial Intelligence (AAAI10), pp.1364-1370 (2010) pdf[49] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Behavioral Analyses of Information Diﬀusion Models by Observed Data of Social Network,” Proceedings ofthe the 2010 International Conference on Social Computing, Behavioral Modeling, and Prediction (SBP 2010), Springer LNCS 6007, pp.149-158 (2010)pdf[50] K. Saito, M. Kimura, K. Ohara and H. Motoda, “Learning Continuous-Time
Information Diﬀusion Model for Social Behavioral Data Analysis,” Proc. of
the First Asian Conference on Machine Learning, Springer LNAI 5828, pp.322337 (2009) pdf

40

[51] K. Saito, M. Kimura and H. Motoda, “Discovering Inﬂuential Nodes for SIS
models in Social Networks,” Proc. of the Twelfth International Conference ofDiscovery Science (DS2009), Springer LNAI 5808, pp.302-316 (2009) pdf[52] M. Kimura, K. Saito and H. Motoda, “Eﬃcient Estimation of Inﬂuence Functions for SIS Model on Social Networks,” Proc. of Twenty-ﬁrst International
Joint Conference on Artiﬁcial Intelligence, pp.2046-2051 (2009) pdf[53] M. Kimura, Saito, R. Nakano and H. Motoda, “Finding Inﬂuential Nodes in a
Social Network from Information Diﬀusion Data,” Proceedings of the second
workshop on on Social Computing, Behavioral Modeling, and Prediction (SBP2009), Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0056-2 18,
pp.138-145 (2009) pdf[54] Kouzou Ohara, Masahiro Hara, Kiyoto Takabayashi, Hiroshi Motoda andTakashi Washio, “Pruning Strategies Based on the Upper Bound of Information Gain for Discriminative Subgraph Mining,”Proc. of PKAW2008, pp.1-11(2008) pdf[55] Takayasu Fushimi, Takashi Kawazoe, K. Saito, M. Kimura, and H. Motoda,“What does an Information Diﬀusion Model Tell about Social Network Structure?,” Proc. of PKAW2008, pp.288-299 (2008) pdf[56] M. Kimura, K. Saito and H. Motoda, “Solving the Contamination Minimization Problem on Networks for the Linear Threshold Model,” Proc. of the
The 10th Paciﬁc Rim International Conference on Artiﬁcial Intelligence (PRICAI08), pp.977-984 (2008) pdf[57] K. Saito, M. Kimura and H. Motoda, “Eﬀective Visualization of Information
Diﬀusion Process over Complex Networks,” Proc. of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in
Databases (ECML PKDD), Part II, pp.326-341 (2008) pdf[58] M. Kimura, K. Saito and H. Motoda, “Minimizing the Spread of Contamination by Blocking Links in a Network,” Proc. of the The 23rd AAAI Conferenceon Artiﬁcial Intelligence (AAAI08), pp.1175-1180 (2008) pdf[59] M. Kimura, K. Yamakawa, K. Saito and H. Motoda, “Community Analysis
of Inﬂuential Nodes for Information Diﬀusion on a Social Network,” Proc.
of IEEE World Congress on Computational Intelligence, pp.1359-1364 (2008)pdf[60] Y. S. Kim, B. H. Kang, P. Compton and H. Motoda, “Search Engine Retrievalof Changing Information,” Proc. of the 16th International World Wide Web
Conference, pp.1195-1196 (2007) pdf[61] H. Motoda,“What Can We Do with Graph-Structured Data? - A Data Mining
Perspective -”, Proc. of AI 2006, A. Sattar and B.H. Kang (Eds.), Springer
LNAI 4304, pp.1-2 (2006) pdf[62] K. Fukata, T. Washio and H. Motoda, “A Method to Search ARX Model
Orders and Its Application to Sales Dynamics Analysis”, Proceedings of Sixth
IEEE International Conference on Data Mining, Workshop on Risk Mining
41

2006, pp.590-595 (2006) pdf[63] K. Takabayashi, P. C. Nguyen, K. Ohara, H. Motoda and T. Washio, “Extracting Discriminative Patterns from Graph Structured Data using Constrained
Search”, Advances in Knowledge Acquisition and Management, A. Hoﬀmann,
B.H. Kang, D. Richards, and S. Tsumoto (Eds.). Springer, LNAI 4303, pp.6474 (2006) pdf[64] K. Takabayashi, P. C. Nguyen, K. Ohara, H. Motoda and T. Washio, “Mining Discriminative Patterns from Graph Structured Data with Constrained
Search”, Proc. of the Workshop on Mining and Learning with Graphs, pp.205212 (2006) pdf[65] T. Washio, K. Nakanishi, H. Motoda, and T. Okada, “Mutagenicity Risk
Analysis by Using Class Association Rules T. Washio et al. (Eds.): JSAI2005 Workshops, LNAI 4012, pp.436-45, Springer-Verlag (2006) pdf[66] T. Washio, Y. Shinnou, K. Yada, H. Motoda and T. Okada,” Analysis on a
Relation between Enterprise Proﬁt and Financial Sate by Using Data Mining
Techniques,” Working Notes of RM2006: Workshop on Risk Mining, Data
Mining for Detection, Analysis and Utilization of Risk Information, Collocatedwith The 20th National Meeting of JSAI2006, pp.35-46 (2006), also appearedin New Frontiers in Artiﬁcial Intelligence, LNCS 4384, Springer-Verlag pp.305316 (2007)[67] K. Takabayashi, P. C. Nguyen, K. Ohara, H. Motoda and T. Washio, “Extracting Discriminative Patterns from Graph Structured Data using Constrained
Search”, Proc. of the Paciﬁc Knowledge Acquisition Workshop, pp. 62-72(2006) pdf[68] P. C. Nguyen, K. Ohara, A. Mogi, H. Motoda and T. Washio, “Constructing Decision Trees for Graph-Structured Data by Chunkingless Graph-Based
Induction ”, Proc. of 10th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2006, Lecture Notes in Artiﬁcial Intelligence,
Springer-Verlag, LNAI 3918, pp. 390-399 (2006) pdf[69] T. Yoshida, R. Shoda and H. Motoda, “Graph Clustering Based on Structural
Similarity of Fragments”, Proc. of the International Workshop on Federation
over the Web, Springer, LNAI 3847, pp. 97-114 (2006) pdf[70] P. C. Nguyen, K. Ohara, A. Mogi, H. Motoda and T. Washio, “Decision Tree
Construction by Chunkingless Graph-Based Induction for Graph-Structured
Data”, Prof. of the International Workshop on Mining Complext Data, pp.6572 (2005) pdf[71] T. Washio, Y. Mitsunaga and H. Motoda, “Mining Quantitative Frequent
Itemsets Using Adaptive Density-based Subspace Clustering”, Proc. of theFifth IEEE International Conference on Data Mining, pp.793-796 (2005) pdf[72] A. Termier, M. Rousset, M. Sebag, K. Ohara, T. Washio and H. Motoda,“Eﬃcient Mining of High Brabching Factor Attribute Trees”, Proc. of theFifth IEEE International Conference on Data Mining, pp.785-788 (2005) pdf
42[73] T. Washio, F. Adachi and H. Motoda, “SCALETRACK: A System to Discover
Dynamic Law Equations Containing Hidden States and Chaos”, Proc. of
the Eighth International Conference on Discovery Science, Lecture Notes in
Artiﬁcial Intelligence, Springer-Verlag, LNAI 3735, pp.253-266 (2005)[74] T. Washio, K. Nakanishi and H. Motoda, “Deriving Class Association Rules
Based on Levelwise Dubspace Clustering”, Proc. of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD),
Lecture Notes in Artiﬁcial Intelligence, Springer-Verlag, LNAI 3721, pp.692700 (2005)[75] A. Termier, M. Rousset, M. Sebag, K. Ohara, T. Washio and H. Motoda,“Computation-time Eﬃcient and Robust Attribute Tree Mining with DRYADEPARENT”, Prof. of Third International Workshop on Mining Graphs, Trees
and Sequences (MGTS), pp.63-76 (2005) pdf[76] T. Washio, F. Adachi and H. Motoda, “Discovering Time Diﬀerential Law
Equations Containing Hidden State Variables and Chaotic Dynamics”, Proc.
of Nineteenth International Joint Conference on Artiﬁcial Intelligence, pp.16421644 (2005)[77] K. Yada, H. Motoda and T. Washio, “A Data Mining for Graph Structure Data Helps to Discover New Knowledge in Consumer Behavior andMakes Proﬁts”, Proc. CD of AMS International Retailing Conference, Reims,
France), pp.1-17 (2005)[78] P. C. Nguyen, K. Ohara, H. Motoda and T. Washio, “Cl-GBI: A Novel Approach for Extracting Typical Patterns from Graph-Structured Data”, Proceedings of 9th Paciﬁc-Asia Conference on Knowledge Discovery and Data
Mining, PAKDD 2005, Lecture Notes in Artiﬁcial Intelligence, Springer-Verlag,
LNAI 3518, Advances in Knowledge Discovery and Data Mining, pp.639-649(2005)[79] T. Yoshida and H. Motoda, “Performance Evaluation of Fusing Two Diﬀerent Knowledge Sources in Ripple Down Rules Method”, Proc. of The Third
International Conference on Active Media Technology (AMT2005), pp.63-68(2005) pdf[80] T. Yoshida, A. Mogi, K. Ohara, H. Motoda and T. Washio, “Reﬁning Diagnostic Knowledge Extracted from Interferon Therapy”, Proc. of The Third
International Conference on Active Media Technology (AMT2005), pp.69-74(2005) pdf[81] T. Washio, K. Nakanishi, H. Motoda, T. Okada, “Mutagenicity Risk Analysis
by Using Class Association Rules”, Proc. of the First International Workshop
on Risk Management Systems with Intelligent Data Analysis (RMDA-2005),
pp.23-34 (2005)[82] K. Yoshida, F. Adachi, T. Washio, H. Motoda, T. Homma, A. Hakashima ,H. Fujikawa and K. Yamazaki, “Memory Management of Density-based Spam
Detector”, Proc. of International Workshop on Computer Intelligence for Exabyte Scale Data Explosion, SAINT2005 Workshops, The 2005 International
43

Symposium on Applications and the Internet (SAINT2005), pp.370-376 (2005)[83] T. Washio, A. Fujimoto and H. Motoda, “A Framework of Numerical Basket
Analysis”, Proc. of International Workshop on Computer Intelligence for Exabyte Scale Data Explosion, SAINT2005 Workshops, The 2005 International
Symposium on Applications and the Internet (SAINT2005), pp.340-343 (2005)[84] M.Kuroda, K.Yada, H.Motoda and T.Washio, “Knowledge Discovery from
Consumer Behavior in an Alcohol Market by Using Graph Mining Technique”,
Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and
IEICE-SIGAI on Active Mining, pp.111-116 (2004)[85] T. Washio, A. Fujimoto and H. Motoda, “Extention of Basket Analysis andQuantitative Association Rule Mining”, Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining,
pp.117-122 (2004)[86] F. Adachi, T. Washio and H. Motoda, “Scientiﬁc Discovery of Dynamic Hidden States and Diﬀerential Law Equations”, Joint Workshop of VietnameseSociety of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active Mining,
pp.175-180 (2004)[87] P. C. Nguyen, K. Ohara, H. Motoda and T. Washio, “Cl-GBI: A Novel Strategy to Extract Typical Patterns from Graph Data”, Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active
Mining, pp.105-110 (2004)[88] A. Mogi, P. C. Nguyen, K. Ohara, H. Motoda and T. Washio, “DT-ClGBI:
Analysis of Hepatitis Dataset by Using Cl-GBI”, Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ and IEICE-SIGAI on Active
Mining, pp.43-48 (2004)[89] K. Yada, H. Motoda, T. Washio and A. Miyawaki, “Consumer Behavior Analysis by Graph Mining Technique”, Proc. of KES2004, Springer Verlag, LNAI
3214, pp.800-806 (2004) pdf[90] K. Ohara, Y. Onishi, N. Babaguchi and H. Motoda, “Constructive Inductive
Learning based on Meta-Attributes”, Proc. of the 7th International Conference on Discovery Science, pp.142-154 (2004)[91] K. Ohara, T. Yoshida, W. Geamsakul, H. Motoda, T. Washio, H. Yokoi and
K. Takabayashi, “Analysis of Hepatitis Dataset by Decision Tree Graph-Based
Induction”, Proc. of Discovery Challenge, Workshop held in conjunction withthe 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) pp.173-184 (2004)[92] P. C. Nguyen, T. Washio, K. Ohara and H. Motoda, “Using a Hash-based
Method for Apriori-based Graph Mining”, Proc. of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD),
pp.349-361 (2004)[93] K. Yoshida, F. Adachi, T. Washio, H. Motoda, T. Homma, A. Hakashima ,H. Fujikawa and K. Yamazaki, “Density-Based Spam Detector”, Proceedings
44

of the Tenth SIGKDD conference, pp.486-493 (2004)[94] T. Yoshida, W. Geamsakul, A. Mogi, K. Ohara, H. Motoda, T. Washio, H.
Yokoi and K. Takabayashi, “Preliminary Analysis of Interferon Therapy by
Graph-Based Induction”, Proc. of the Third International Workshop on Active Mining (AM-2004), pp.31-40 (2004)[95] H. Motoda, T. Yoshida, K. Ohara, W. Geamsakul, T. Washio, H. Yokoi and
K. Takabayashi, “Application of DT-GBI to Promoter and Hepatitis Datasets,
Proc. of the Knowledge Discovery in BioMedicine, pp. 10-40 (2004)[96] A. Mandvika, H. Liu and H. Motoda, “Constructing Compact Dual Ensembles
for Eﬃcient Active Learning”, Proc. of the Paciﬁc Knowledge Acquisition
Workshop, ISBN No. 1-877314-34-X, pp.29-43 (2004) pdf[97] A. Mandvika, H. Liu and H. Motoda, “Compact Dual Ensembles for Active
Learning”, Proc. of 8th Paciﬁc-Asia Conference on Knowledge Discovery andData Mining, LNAI3056, pp.293-297 (2004) pdf[98] W. Geamsakul, T. Matsuda, T. Yoshida, H. Motoda and T. Washio, “ Performance Evaluation of Decision Tree Graph-Based Induction”, Sixth International Conference on Discovery Sience, Lecture Notes in Artiﬁcial Intelligence,
LNAI 2874, Springer, pp.128-140 (2003)[99] W. Geamsakul, T. Matsuda, T. Yoshida, H. Motoda and T. Washio, “ Constructing a Decision Tree for Graph Structured Data”, Proc. of First International Workshop on Mining Graphs, Trees and Sequences (MGTS-2003), 14th
European Conference on Machine Learning (ECML’03) and 7th European
Conference on Principles and Practice of Knowledge Discovery in Databases(PKDD’03) pp.1-10 (2003)[100] A. Inokuchi, T. Washio and H. Motoda, “Speciﬁc Biasis for Mining Frequent
Substructures”, Proc. of First International Workshop on Mining Graphs,
Trees and Sequences (MGTS-2003), 14th European Conference on Machine
Learning (ECML’03) and 7th European Conference on Principles and Practiceof Knowledge Discovery in Databases (PKDD’03) pp.45-54 (2003)[101] F. Adachi, T. Washio, H. Motoda and H. Hanafusa, “Development of GenericSearch Method Based on Transformation Invariance”, Proc. of the 14th International Symposium on Methodologies for Intelligent Systems, ISMIS03, pp.
486-495 (2003)[102] W. Geamsakul, T. Yoshida, K. Ohara, H. Motoda and T. Washio, “Extracting Diagnostic Knowledge from Hepatitis Dataset by Decision Tree GraphBased Induction”, Proc. of International Workshop on Active Mining 2003,
pp. 106-117 (2003).
[103] W. Geamsakul,T. Matsuda, T. Yoshida, H. Motoda and T. Washio, ”Classiﬁer Construction by Graph-Based Induction for Graph-Structured Data”,
Proceedings of 7th Paciﬁc-Asia Conference on Knowledge Discovery and Data
Mining, LNAI2637, pp.52-62 (2003)[104] H. Liu, L. Yu, D. Manoranjan and H. Motoda, ”Active Feature Selection
45

Using Classes”, Proceedings of 7th Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining, LNAI2637, pp.474-485 (2003) pdf[105] T. Yoshida, T. Wada, H. Motoda and T. Washio, “Adaptive Ripple Down
Rules Method base on Minimum Description Length Principle”, Proceedingsof 2002 IEEE International Conference on Data Mining, pp.530-537 (2002)[106] T. Matsuda, T. Yoshida, H. Motoda and T. Washio, “Active Mining from
Hepatitis Data by Beam-wise GBI”, Proceeding of International Workshop on
Active Mining, pp. 37-44 (2002)[107] F. Adachi, T. Washio, H. Motoda and T. Washio, “Development of GenericSearch Method Based on Transformation Invariance”, Proceeding of International Workshop on Active Mining, pp. 52-57 (2002)[108] T. Matsuda, H. Motoda, T. Yoshida and T. Washio, “Preliminary Analysis of Hepatitis Data by Beam-wise Graph-Based Induction”, The Discovery
Challenge Workshop, ECML/PKDD 2002 (2002)[109] T. Matsuda, H. Motoda, T. Yoshida and T. Washio, “Knowledge Discovery
from Structured Data by Beam-wise Graph-Based Induction”, Proc. of Seventh Paciﬁc Rim International Conference on Artiﬁcial Intelligence, Springer,
LNAI 2417, pp.255-264 (2002)[110] T. Wada, T. Yoshida, H. Motoda and T. Washio, “Extension of the RDR
method that can Adapt to Environmental Changes and Acquire Knowledge
from Both Experts and Data”, Proc. of Seventh Paciﬁc Rim International
Conference on Artiﬁcial Intelligence, Springer, LNAI 2417, pp.218-227 (2002)[111] K. Fujiwara, T. Yoshida, H. Motoda and T. Washio, “Case Generation
Method for Constructing an RDR Knowledge Base”, Proc. of Seventh Paciﬁc Rim International Conference on Artiﬁcial Intelligence, Springer, LNAI
2417, pp.228-237 (2002)[112] T. Matsuda, H. Motoda, T. Yoshida and T. Washio, “Beam-wise GraphBased Induction for Structured Data Mining”, Proc. of Paciﬁc Rim Knowledge
Acquisition Workshop (PKAW2002), pp.174-188 (2002)[113] T. Wada, T. Yoshida, H. Motoda and T. Washio, “Reorganizing Knowledge
Base of RDR for Adaptation to Environmental Changes”, Proc. of Paciﬁc
Rim Knowledge Acquisition Workshop (PKAW2002), pp.189-202 (2002)[114] T. Matsuda, H. Motoda, T. Yoshida and T. Washio, “Mining Patterns from
Structured Data by Beam-wise Graph-Based Induction”, Workshop Notes on
Multi-Relational Data Mining, KDD-2002, pp.113-129 (2002)[115] H. Motoda and H. Liu, “Feature Selection, Extraction and Construction”,
Invited, Proc. of Foundation of Data Mining, PAKDD02, pp.67-72 (2002) pdf[116] T. Washio and H. Motoda, “A Method to Discover Admissble Model Equations from Observed Data”, Working Notes of 4th Internationaln Workshop
on Similarity Methods, pp.231-246 (2001)[117] M. Tsukada, T. Washio and H. Motoda, “Automatic Web-Page Classiﬁcation46

by Using Machine Learning Methods”, Web Intelligence Research and Development (Proc. Of First Asia Paciﬁc Conf., WI2001), LNAI2198, Springer,
pp.303-313 (2001)[118] T. Ikeda, T. Washio and H. Motoda, “Basket Analysis on Meningitis Data”,
New Frontiers in Artiﬁcial Intelligence, Joint JSAI 2001 Workshop Post-Proceedings,
LNAI 2253, pp.516-524 (Working Notes of JSAI KDD Challenge 2001, JKDD01,
pp.33-40 (2001)[119] T. Washio and H. Motoda, “Discovering Admissible Simultaneous Equation
Models from Observed Data”, Machine Learning: ECML2001, Proc. of the
12th European Conference on Machine Learning, Springer, pp.539-551 (2001)
pdf[120] M. Terabe, T. Washio and H. Motoda, “The Eﬀect of Subsampling Rateon S3 Bagging Performance”, Proc. of Active Learning, Database Sampling,
Experimental Design: Views on Instance Selection, pp. 48-55 (Workshop of
ECML/PKDD2001 (2001)[121] M. Terabe, T. Washio and H. Motoda, “S3 Bagging: Fast ClassiﬁerInduction Method with Subsampling and Bagging”, Advances in Intelligent Data
Analysis, Proc. of the Fourth International Symposium on Intelligent Data
Analysis, Springer, pp. 177-186 (2001)[122] T. Washio and H. Motoda, “Discovery of Law Equations governing HumanAﬃnity under Trade-oﬀ between Cost and Risk”, Proc. of International Meeting of The Psychometric Society (IMPS-2001), p.74 (2001) pdf[123] T. Washio and H. Motoda, “A Method to Discover Admissble Model Equations from Observed Data”, Working Notes of 4th Internationaln Workshop
on Similarity Methods, pp.231-246 (2001)[124] T. Wada, H. Motoda and T. Washio, “Knowledge Acquisition from Both
Human Expert and Data”, Proc. of the Fifth Paciﬁc-Asia Conference on
Knowledge Discovery and Data Mining (PAKDD01), Lecture Notes in Artiﬁcial Intelligence, Springer-Verlag, pp.550-561 (2001)[125] T. Wada, H. Motoda and T. Washio, “Integrating Inductive Learning andKnowledge Acquisition in the Ripple Down Rules Method,” Proc. of the
6th Paciﬁc Rim Knowledge Acquisition Workshop (PKAW2000), pp.325-340(2000)[126] T. Matsuda, T. Horiuchi, H. Motoda and T. Washio, “Graph-Based Induction for General Graph Structured Data and Its Application to ChemicalCompound Data”, Proc. of the Third International Conference of Discovery
Science (DS2000), pp.99-111 (2000)[127] T. Washio and H. Motoda, “Modeling Admissible Simultaneous Equation
Systems Based on Complete Subsets and Scale-Type Constraints”, Working
note of Similarity Methods: 3rd International Workshop, pp. 73-84 (2000)
pdf[128] A. Inokuchi, T. Washio and H. Motoda, “An Apriori-Based Algorithm for47

Mining Frequent Substructures from Graph Data”, Proc. of PKDD2000:
Principles of Data Mining and Knowledge Discovery, 4th Europian Conference, Lecture notes in Artiﬁcial Intelligence 1910, Jan Zytkow Eds., pp.13-23(2000) pdf[129] T. Washio, H. Motoda and Y. Niwa, “Enhancing the Plausibility of Law
Equation Discovery”, Proceedings of the Seventeenth International Conference
on Machine Learning (ICML2000), pp.1127-1134 (2000) pdf[130] H. Motoda, T. Washio, T. Horiuchi, A. Inokuchi and T. Matsuda, “Mining Patterns from Graph Structured Data”, Proc. of the Fifth International
Workshop on Multistrategy Leearning (MSL2000), pp.137-150 (2000)[131] A. Inokuchi, T. Washio, T. Okada and H. Motoda, “Mining Algebraic Mining
Method of Graph Substructures to Mutagenisis Data Analysis”, Proc. of theInternational Workshop of KDD Challenge on Real-world Data, PAKDD2000,
pp.41-46 (2000)[132] T. Matsuda, T. Horiuchi, H. Motoda and T. Washio, “Extension of GraphBased Induction for General Graph Structured Data”, Proc. of the Fourth
Paciﬁc-Asia Conference of Knowledge Discovery and Data Mining (PAKDD2000),
pp.420-431 (2000)[133] M. Dash, H. Liu and H. Motoda, “Consistency Based Feature Selection”,
Proc. of the Fourth Paciﬁc-Asia Conference of Knowledge Discovery andData Mining (PAKDD2000), pp.98-109 (2000) pdf[134] T. Washio, H. Motoda and Y. Niwa, “Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constraints”,
Proc. of IJCAI-99: the Sixteenth International Joint Conference on Artiﬁcial
Intelligence, pp.772-779 (1999) pdf[135] T. Washio and H. Motoda, “Extension of Dimensional Analysis for Scaletypes and its Application to Discovery of Admissible Models of Complex
Processes”, Working Notes of the 2nd International Workshop on Similarity
Method, pp. 129-147 (1999) pdf[136] M. Terabe, O. Katai, T. Sawaragi, T. Washio and H. Motoda, “A Data
Pre-processing Method Using Association Rules of Attributes for Improving
Decision Tree”, Proc. of the Third Paciﬁc-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD99), Lecture Notes in Computer Science
Series, Springer-Verlag, pp.143-147 (1999)[137] T. Matsuda, T. Horiuchi, H. Motoda, T. Washio, K. Kumasawa, and N. Arai,“Graph-Based Induction for General Graph Structured Data”, Proceedings ofthe 2nd International Conference, Discovery Science 1999 (DS’99): Lecture
Notes in Artiﬁcial Intelligence 1721, pp. 340-341 (1999)[138] A. Inokuchi, T. Washio and H. Motoda, “Derivation of the Topology Structure from Massive Graph Data”, Proceedings of the 2nd International Conference, Discovery Science 1999 (DS’99): Lecture Notes in Artiﬁcial Intelligence
1721, pp. 330-331 (1999)
48

[139] T. Washio, H. Motoda and Y. Niwa, “Automated Scientiﬁc Modeling from
Observed Data and its Application to Socio-Psychology”, Working notes of
QR’99: The Thirteenth International Workshop on Qualitative Reasoning,pp. 240-249 (1999) pdf[140] A. Inokuchi, T. Washio and H. Motoda, “Basket Analysis for Graph Structured Data”, Proc. of the Third Paciﬁc-Asia Conference on Knowledge Discovery and Data Mining (PAKDD99), Lecture Notes in Computer Science
Series, Springer-Verlag, pp.420-431 (1999)[141] T. Wada, T. Horiuchi, H. Motoda and T. Washio, “Characterization of Default Knowledge in Ripple Down Rules Method”, Proc. of the Third PaciﬁcAsia Conference on Knowledge Discovery and Data Mining (PAKDD99), Lecture Notes in Computer Science Series, Springer-Verlag, pp.284-295 (1999)[142] H. Motoda, “Computer Assisted Discovery of First Principle Equations from
Numeric Data”, Proc. of the Third Paciﬁc-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD99), Lecture Notes in Computer Science
Series, Springer-Verlag, pp.2-2 (1999)[143] K. Yoshida and H. Motoda, “Table, Graph and Logic for Induction”, MachineIntelligence, Vol.15, pp 298-311, Oxford Univ. Press (1999)[144] T. Washio and H. Motoda, “Development of SDS2: Smart Discovery System for Simultaneous Equation Systems”, Discovery Science, Lecture Notes
in Artiﬁcial Intelligence 1532, Springer, pp.352-363 (1998) pdf[145] T. Wada, T. Horiuchi, H. Motoda and T. Washio, “A New Look at Default
Knowledge in Ripple Down Rules Method”, Proc. of Paciﬁc Rim Knowledge
Acquisition Workshop (PKAW), pp.171-186 (1998)[146] T. Washio and H. Motoda, “Discovering Admissible Simultaneous Equationsof Large Scale Systems, Proc. of AAAI-98, Fifteenth National Conference on
Artiﬁcial Intelligence, pp. 189-196 (1998) pdf[147] T. Washio and H. Motoda, “Structured Evaluation Based on Axiomatic Measurement, ” Proc. of PRESTO: Information and Human Activity Workshop,
pp.14-17 (1998)[148] H. Liu, H. Motoda and M. Dash, “A Monotonic Measure for Optimal Feature
Selection”, Machine Learning: ECML-98, Lecture Notes in Artiﬁcial Intelligence, Springer, pp.101-106 (1998) pdf[149] T. Washio, H. Matsuura and H. Motoda, “Mining Association Rules for Estimation and Prediction”, Research and Development in Knowledge Discovery and Data Mining, Lecture Notes in Artiﬁcial Intelligence 1394, Springer,
pp.417-419 (1998)[150] S. Hori, Y. Kawashima, T. Yukimatsu, H. taki, T. Washio and H. Motoda A
Watchdog System for Field Quality - A Basket Analysis Approach - US-Japan
FA Symposium, Vol.2, pp.741-748 (1998)[151] H. Motoda and K. Yoshida, Machine Learning Techiniques to Make Com-49

puters Easier to Use, Proc. of IJCAI’97: Fifteenth International Joint Conference on Artiﬁcial Intelligence, pp.1622-1631, Nagoya Congress Center, Nagoya,Japan (1997)[152] T. Washio and H. Motoda,“Discovering Admissible Models of Complex Systems Based on Scale-Types and Identity Constraints,” Proc. of IJCAI’97:
Fifteenth International Joint Conference on Artiﬁcial Intelligence, pp.810-819,
Nagoya Congress Center, Nagoya, Japan (1997) pdf[153] H. Motoda, T. Washio, T. Kayama and K. Yoshida, “Extracting Behavioral
Patterns from Relational History Data, ” Proc. of the Workshop on Machine
Learning for User Modeling held in conjunction with Sixth International Conference on User Modeling, pp. 6-1, 6-6, Chia Laguna, Sardinia, 2-5 June
(1997)[154] T. Washio and H. Motoda, “Discovery of First Principle Based on DataDriven Reasoning”, Proc. of the First Paciﬁc-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD97), pp. 169-182 (1997)[155] T. Washio and H. Motoda, “Discovery of Possible Law Formulae based on
Measurement Scale”, Proc. of the Fourth International Workshop on RoughSets, Fuzzy Sets, and Machine Discovery, pp.209-216 (1996)[156] T. Washio, H. Motoda and M. Kitamura,“Decision Process Modeling Basedon Consensus Among Fuzzy Integral and AHP,” Proceeding of the 4th International Conference on Soft Computing, Vol.1, pp.308-311 (October, 1996)
Kyushu Technical Institute，Izuka，Kyushu[157] B.H. Kang, K. Yoshida, H. Motoda, M. Iwayama and P. Compton, “HelpDesk System with Intelligent Interface”, Proc. of the 1966 Paciﬁc Rim Knowledge Acquisition Workshop, pp. 313-332 (1996)[158] H. Motoda, “Application of Graph-Based Induction to Automated User Modeling, ” Proc. of Machine Learning meets Human Computer Interaction, pp.14 (1996)[159] T. Washio and H. Motoda, “Discovery of Possible Law Equations by Combined Use of Scale-Based and Data-Driven Reasoning”, Proc. of the 1966
Paciﬁc Rim Knowledge Acquisition Workshop, pp. 130-149 (1996)[160] T. Washio and H. Motoda, “A History-oriented Envisioning Method”, PRICAI’96: Topics in Artiﬁcial Intelligence, Lecture Notes in Artiﬁcial Intelligence 1114 (Proc. of the Fourth Paciﬁc Rim International Conference on
Artiﬁcial Intelligence, Cairns, Australia, Aug. 26-30), pp. 312-323 (1996)[161] T. Washio and H. Motoda, “Scale-based Reasoning on Possible Law Equations”, Proc. of the Tenth International Workshop Qualitative Reasoning,
Stanford Sierra Camp, Fallen Leaf Lake, California, pp.255-264 (1996)[162] H. Motoda, “Can Machine Learn in the Same Way as Humand Do?,” Proc. ofFirst Interenational Conference on Applied Ergonomics, (Advances in Applied
Ergonimics, A. F. Özok and G Salvendy (Editors), USA Publishing), pp.479484 (1996)50

[163] S. Nishioka, A. Kawaguchi, and H. Motoda, “Process labeled kernel proﬁling:a new facility to proﬁle system activities,” Proc. of USENIX 1996 Annual
Techincal Conference, pp.295-306 (1995)[164] H. Narayanan, M. Suwa, and H. Motoda, “Diagram-based Problem Solving: The Case of an Impossible Problem,” Proc. of the Seventeenth Annual
Conference of the Cognitive Science Society, pp.206-211 (1995)[165] K. Yoshida and H. Motoda, “Automated user Modeling for Intelligent Interface, Proc. of the Sixth International Conference on Human-Computer
Interaction, (Symbiosis of Human and Artifact, Y. Anzai, K. Ogawa and H.
Mori (Editors), Elsevior Science B.V.), pp.1103-1108 (1995)[166] M. Suwa and H. Motoda, “On Dealing with the Dynamic Utility of Learned
Knowledge,” Handout of International Workshop on Machine Intelligence (1993),
Revised paper in Machine Intelligence, Vol.14, pp.111-130, Oxford Univ. Press(1995)[167] A. Kawaguchi, S. Nishioka and H. Motoda, “A Flash-Memory Based File
System,” Proc. of USENIX 1995 Annual Techincal Conference, pp.155-164(1995)[168] K. Yoshida and H. Motoda, “User Model Acquisition for Intelligent User
Interface,” Proc. of the Third Japanese Knowledge Acquisition for KnowledgeBased Systems Workshop (JKAW94 ), pp.107-119 (1994)[169] H. Narayanan, M. Suwa, and H. Motoda, “How Things Appear to Work:
Predicting Behaviors from Device Diagrams,” Proc. of Twelveth National
Conference on Artiﬁcial Intelligence (AAAI-94), Vol. 2, pp.1161-1167 (1994)[170] H. Narayanan, M. Suwa, and H. Motoda, “Qualitative Behavior Hypothesis from Device Diagrams,” Proc. of the Eighth International Workshop on
Qualitative Reasoning about Physical Systems, pp.197-204 (1994)[171] M. Suwa and H. Motoda, “PCLEARN: A model for learning perceptualchunks,” Proc. of the Sixteenth Annual Conference of the Cognitive Science
Society, pp.830-835 (1994)[172] H. Narayanan, M. Suwa, and H. Motoda, “A Study of Diagrammatic Reasoning from Verbal and Gestural Data,” Proc. of the Sixteenth Annual Conference of the Cognitive Science Society, pp.652-657 (1994)[173] M. Iwayama, N. Indurkhya and H. Motoda, “A New Algorithm for AutomaticConﬁguration of Hidden Markov Models and its Application to Transmembrane Domain Classiﬁcation,” Proc. of ’93 Japan/Korea Joint Conference on
Expert Systems, pp.319-324 (1994)[174] M. Suwa and H. Motoda, “Learning Perceputually-chunked Macro-operators”,
Handout of International Workshop on Machine Intelligence (1992), Revised
paper in Machine Intelligence, Vol.13, pp.419-440, Oxford Univ. Press (1994)[175] K. Yoshida, H. Motoda and N. Indurkhya, “Unifying Learning Methods by
Colored Digraphs,” Proc. of ALT93, pp.342-355 (1993)51

[176] M. Suwa and H. Motoda, “A perceptual criterion for visually controlling
learning,” Proc. of ALT93, pp.356-369 (1993)[177] M. Iwayama, N. Indurkhya and H. Motoda,“A New Algorithm for AutomaticConﬁguration of Hidden Markov Models,” Proc. of ALT93, pp.237-250 (1993)[178] K. Yoshida, H. Motoda and N. Indurkhya, “Unifying Learning Methods by
Colored Digraphs,” Proceedings of IJCAI-93 workshop on Machine Learning
and Knowledge Acquisition, August, pp.253-269 (1993)[179] Hari N. Narayanan and H. Motoda, “Behavior Hypothesis from Schematic
Diagrams: A Hybrid Approach”, Proceedings of IJCAI-93 workshop on Principles on Hybrid Representation and Reasoning, August, pp.50-61 (1993)[180] M. Suwa and H. Motoda, “A perceptual criterion for visually controlling
learning,” Proceedings of IJCAI-93 workshop on Principles on Hybrid Representation and Reasoning, August, pp.72-83 (1993)[181] H. Motoda, K. Yoshida and M. Suwa, “Some Insights into the Nature of
Learning from Machine Learning Studies”, Proceedings of the 1993 Korea/Japan
Joint Conference on Expert Systems, pp.288-302 (1993)[182] M. Suwa and H. Motoda, “Learning Perceputually-chunked Macro-operators”,
Handout of International Workshop on Machine Intelligence (1992), Revised
paper in Machine Intelligence, Vol.13, pp.419-440, Oxford Univ. Press (1994)[183] A. Levy, Y. Iwasaki and H. Motoda, “Relevance Reasoning to Guide Compositional Modeling”, Presented at The Sixth International Workshop on Qualitative Reasoning about Physical Systems, Edinburgh, Scotland (1992)[184] A. Levy, Y. Iwasaki and H. Motoda, “Relevance Reasoning to Guide Compositional Modeling”, Working Note on Approximation and Abstraction ofComputational Theories, AAAI-92 Workshop, San Jose (1992)[185] M.Suwa and H.Motoda, “Primitive Matching Approach: A Calculation Modelfor Finding Analogical Correspondences in Metaphorical Sentences –Preliminary
Experimental Results,” Proc. of CSLI/IAP Japan Forum, Sep.10-11, Tokyo(1992)[186] A. Levy, Y. Iwasaki and H. Motoda, “Relevance Reasoning to Guide Compositional Modeling”, Proc. of the Second Paciﬁc Rim International Conferenceon Artiﬁcial Intelligence (PRICAI92), pp.910-916, Korea, 1992
[187] K. Yoshida and H. Motoda,“Automatic Knowledge Reformulation,” Proc.
of the Second Japanese Knowledge Acquisition for Knowledge-Based Systems
Workshop (JKAW92), pp.279-293 (1992)[188] A. Levy, H. Motoda and Y. Iwasaki,“Acquiring (Ir)relevance Knowledge for
Problem Solving”, Proc. of the Second Japanese Knowledge Acquisition forKnowledge-Based Systems Workshop (JKAW92), pp.263-278 (1992)[189] K. Yoshida and H. Motoda,“Knowledge Reformation and Concept Hierarchy
Formation”, Symposium of Knowledge Reformation, IPSJ, SIG-AI, pp.79-88(1991)
52

[190] M. Suwa and H. Motoda, “Learning Metaphorical Relationships between
Concepts based on Semantic Representation using Abstract Primitives”, Proc.
of IJCAI Workshop on Non-literal Language: Metaphor, Metonymy, Idion,
Speech Acts, Implicature, pp.123-131 (1991)[191] M.Suwa and H.Motoda, “Learning Abductive Strategies from an Example”,
Invited, Proc. of the workshop on empirical machine learning, pp.36-43 (1991)[192] M. Suwa and H. Motoda, “The Use of Abstract Primitives in Representing
the Meaning of “Verbs” for Understanding Metaphors”, Proc. of ALT91,
pp.231-242 (1991)[193] M. Suwa and H. Motoda, “Acquiring Strategic Knowledge for Overcoming
Imperfect Situations”, Proc. of AKAW91, pp.1-16 (1991)[194] M. Suwa and H. Motoda, “Learning Metaphorical Relationships Between
Concepts based on Semantic Representation Using Abstract Primitives ”,
Working Note of the AAAI-91 Workshop on “Towards Domain-independent
Strategies for Abduction”, pp.72-79 (1991)[195] M. Suwa and H. Motoda, “Learning Abductive Strategies from an Example”,
Working Note of the AAAI-91 Workshop on “Towards Domain-independent
Strategies for Abduction” (1991)[196] A. Kawaguchi, H. Motoda and R. Mizoguchi, “An Architecture of Knowledge
Acquisition by Interview based on Dynamic Analysis,” Proc. of JKAW-90,
pp.81-96 (1990)[197] S. Kobayashi, T. Terano, H. Motoda and R. Mizoguchi, “Research Activities
of Knowledge Acquisition and Learning in Japan ,” Invited, Proc. of JKAW90, pp.113-133 (1990)[198] K. Yoshida and H. Motoda, “ Hierarchical Knowledge Representation basedon Approximation,” Proc. of JKAW-90, pp.345-360 (1990)[199] K. Yoshida and H. Motoda, “Towards Automatic Generation of HierarchicalKnowledge Base,” Working Note of the AGAA-90 Workshop, pp.98-109 (1990)[200] H. Motoda, “Towards Deep Understanding in Problem Solving,” Invited,
Proc. of The First International Conference on Supercomputing in Nuclear
Applications (SNA’90), pp.569-574 (1990)[201] M. Suwa and H. Motoda, “Understanding Metaphors by Frustration- Based
Learning Method,” Proc. of Fourth Knowledge Acquisition for KnowledgeBased Systems Workshop, pp.33 1-33 17 (1989)[202] A. Sakurai and H. Motoda, “Proving Deﬁnite Clauses without Explicit Useof Inductions,” Logic Programming ’88, Lecture Note in Artiﬁcial Intelligence383, Springer Verlag, pp.11-26 (1989)[203] M. Suwa and H. Motoda, “Acquisition of Associative Knowledge by the
Frustration-Based Learning Method in an Auxiliary-Line Problem,” Presented
at the 3rd Knowledge Acquisition for Knowledge Based Systems Workshop(1988)
53

[204] A. Sakurai and H. Motoda, “Proving Deﬁnite Clauses without Explicit Useof Induction ,” in Japanese, Proc of The Logic Programming Conference ’88,
pp.29-38 (1988)[205] T. Kiguchi, H. Motoda, N. Yamada and K. Yoshida,“A Knowledge Based
System for Plant Diagnosis,” Proc. of Int. Topical Mtg. of ANS on Computer
Application for Nuclear Power Plant Operation and Control, Pasco, Washington, U.S.A. (Sep. 1985)[206] H. Motoda, N. Yamada and K. Yoshida, “A Knowledge Based System for
Plant Diagnosis,” Proc. of FGCS-84, pp.582-588 (1984)[207] H. Motoda, N. Yamada and K. Yoshida,“Preliminary Study of Diagnosis
through Knowledge about Systems Descriptions,” Tran. Am. Nucl. Sci.,
Vol.41, pp.294-295 (1984)[208] T. Kiguchi, K. Yoshida, H. Motoda and S. Kobayashi, “A Method of Plant
Diagnosis by Knowledge Engineering Technique,” Proc. of Enlarged Halden
Programme Group Meeting, OECD Halden Reactor Project, pp.G-2/1-G-2/7,
Loen, Norway (May 1983)[209] N. Yamada and H. Motoda, “A Diagnosis Method of Dynamic System Using
the Knowledge on System Description,” Proc. of Eight International Joint
Conference on Artiﬁcial Intelligence (IJCAI-83), pp.225-229 (1983)[210] H. Motoda, Y. Bessho, T. Hayase and K. Kato, “Multi-Region Neutronics
Model for Slow Transient Analysis of BWRs,” Trans. Am. Nucl. Soc., Vol.38,
pp.352-354 (1981) pdf[211] O. Yokomizo, I. Sumida and H. Motoda, “Development of a Time-Domain
BWR Core Stability Analysis Program,” Trans. Am. Nucl. Soc., Vol.34,
pp.605-606 (1980) pdf[212] T. Kiguchi, T. Hayase, Y. Bessho, H. Motoda, S. Kobayashi and T. Hoshi,“Eﬀective Use of Operating Data for Three-Dimensional BWR Core Simulator,” Paper presented at NEACRP Specialists’ Meeting on Calculation of 3Dimensional Rating Distribution in Operating Reactors, Paris, France (Nov.,
1979)[213] T. Enomoto, Y. Bessho, T. Kiguchi, H. Motoda, T. Hayase and H. Hiranuma,“Development of Startup Control Rod Programming Code System for BWRs,”
Trans. Am. Nucl. Soc., Vol.30, pp.648-648 (1978) pdf[214] T. Kiguchi, H. Motoda, S. Kobayashi, S. Uchikawa, M. Yokomi, H. Iida
and H. Nakamura, “On-Line Core Performance Evaluation and Operating
Guidance System for Boiling Water Reactors,” Proc. of IAEA International
Symposium on Nuclear Power Plant Control and Instrumentation, IAEA-SM226/30, pp.1-23, Cannes, France (April, 1978) pdf[215] T. Kiguchi, H. Motoda and S. Kobayashi, “Feasibility Study of Core Management System by Data Communication for Boiling Water Reactors, ” Paper presented at IAEA/NPPCI Specialists’ Meeting on Nuclear Power Plant
Control Problems Associated with Load Following and Network Transients,
54

Cadarache, France (Jan., 1977)[216] T. Kiguchi, T. Fukuzaki, Y. Nishizawa and H. Motoda, “Evaluation of OnLine Power Distribution Prediction Method by BWR Operating Data,” Trans.
Am. Nucl. Soc., Vol.27, pp.728-730 (1977) pdf[217] T. Hayase and H. Motoda, “BWR Control Rod Programming Using Heuristic
and Mathematical Methods,” Trans. Am. Nucl. Soc., Vol.27, pp.727-728(1977) pdf[218] M. Serizawa, H. Motoda, S. Kobayashi, S. Yamada, H. Iida, M. Yokomi and
H. Nakamura, “Experience with Computer Based Systems Applied to Boiling
Water Reactor Power Plant,” Proc. of Joint Automatic Control Conference,FP21-3:50, pp.360-366, San Francisco (June, 1977) pdf[219] Y. Nishizawa, T. Kiguchi and H. Motoda, “On-Line Core Performance Prediction of BWR,” Trans. Am. Nucl. Soc., Vol.22, pp.242-243 (1975) pdf[220] T. Kiguchi and H. Motoda, “Development of Computer Program STROD
for Startup Control Rod Programming of BWR,” Paper presented at IAEA
Specialists’ Meeting on Spatial Control Problems, Nyköping, Sweden (Oct.,1974) pdf
[221] T. Kiguchi and H. Motoda, “Development of Computer Program INROD forIntermediate Term Control Rod Programming of BWR,” Paper presented at
IAEA Specialists’ Meeting on Spatial Control Problems, Nyköping, Sweden(Oct., 1974) pdf[222] H. Motoda, T. Kiguchi and T. Kawai, “Computer Program for Control Rod
Programming of BWR,” Trans. Am. Nucl. Soc., Vol.16, pp.171-172 (1973)
pdf[223] H. Motoda, T. Kiguchi and T. Kawai, “Optimal Rod Programming andLoading Pattern in a Multiregion Reactor,” Trans. Am. Nucl. Soc., Vol.15,
pp.105-106 (1972) pdf[224] M. Senoh and H. Motoda, “Hybrid Simulation of Nuclear Reactor for Optimal Fuel Management,” Proc. of AICA Symposium, pp.G-3/1-G-3/5, Tokyo(April, 1971) pdf
3. Review articles in journals[1] K. Ohara, K. Saito, M. Kimura and H. Motoda, “Analysis of Inﬂuence overSocial Network based on Information Diﬀusion,” (in Japanese) J. of the Operations Research Society of Japan, Vol.8, pp.15-21 (2015)[2] H. Liu and H. Motoda, “Less is More,” in Computational Methods of Feature
Selection, Chapman & Hall/CRC, Data Mining and Knowledge Discovery
Series, pp.3-17 (2008)[3] H. Motoda, H. T. bao, T. Washio, K. Yada, T. Yoshida and K. Ohara, “Active
Mining for Structured data (in Japanese)”, J. of JSAI, Vol.20, No.2, pp.172179 (2005) pdf

55

[4] T. Washio and H. Motoda, “State of the Art of Graph-based Data Mining”,
SIGKDD Explorations, Editorial: Multi-Relational Data Mining: The Current Frontiers , Editors: Saso Dzeroski and Luc De Raedt, SIGKDD Exploration, July 2003. Vol. 5, Iss. 1, pp.59-68[5] H. Motoda, “Active Mining - A Spiral Model of Knowledge Discovery fromData”, Proc. of the 19th Fuzzy Symposium, pp. 39-45 (2003)[6] T. Washio and H. Motoda, “State of the Art of Graph-based Data Mining”,
SIGKDD Explorations, Editorial: Multi-Relational Data Mining: The Current Frontiers , Editors: Saso Dzeroski and Luc De Raedt, July 2003. Vol. 5,
Iss. 1, pp.59-68 (2003)[7] H. Motoda, ”Comments on ”Histrical Roadmap of Research on Genetic Algorithms” (and Their Answers),” J. of JSAI, Vol.18, No.6, pp.739-740 (2003)
pdf[8] T. Okada and H. Motoda, “Association Rules and Its Vicinity”, Operations
Research (in Japanese)”, Operations Research, Vol.47, No.9, pp.565-571 (2002)
pdf[9] H. Motoda, M. Numao, T. Yamaguchi and S. Tsumoto, “The Active Mining
Project (in Japanese)”, J. of JSAI, Vol.17, No.5, pp.615-621 (2002) pdf[10] H. Motoda, M. Numao, T. Onoda and H. Kawano, ”On the Special Issue:
Active Mining,” J. of JSAI, Vol.17, No.5, pp.614 (2002) pdf[11] H. Motoda and T. Washio, “Perspective of Data Mining”, Systems , Control
and Information, Vol.46, No.4, pp.169-176 (2002) pdf[12] H. Motoda, ”Inductive Logic Programming (K. Furukawa, T. Ozaki and K.
Ueno), ” Book Review in Japanese, J. of JSAI, Vol.16, No.6 , pp.912 (2001)
pdf[13] T. Washio and H. Motoda，“Challenge of Law Discovery by Computer,” Applied Mathmatical Science，Vol.11, No.1, pp.59-62 (2001) pdf[14] H. Motoda, “Editor’s Indtoruction to Discovery Science (in Japanese),” Vol.15,
No.4, pp.592-594 (2000) pdf[15] T. Washio and H. Motoda, “Evolution of Scientiﬁc Discovery by Computer,“ Bit, Special Issue, pp.207-216, May (2000)[16] H. Motoda and T. Washio, “Discovery of Laws, “ICIE Transactions of Information and Systems, Vol.E83-D, No.1, pp.44-51 (2000)[17] H. Motoda and S. Arikawa, “Special Feature on Discovery Science,” New
Generation Computing, Vol.18, No.1, pp.13-16 (2000) pdf[18] H. Motoda, “Comments on “Fascinated by Explicit Understanding” and Their
Answers (in Japanese),” J. of JSAI, Vol.14, No.5, pp.814-818 (1999) pdf[19] H. Motoda, “Fascinated by Explicit Understanding (in Japanese),” J. of JSAI,
Vol.14, No.4, pp.615-625 (1999) pdf[20] T. Washio and H. Motoda “Theory of Scale Types, ” Journal of JJSFTS
56

(Japan Society for Fuzzy Theory and Systems), Vol.10, No.3, pp.401-413(1998) pdf[21] H. Motoda, ”Comments on AI Map – Form and Content – Invitation toContent-Oriented AI Research,” J. of JSAI, Vol.11, No.4, pp.555-557 (1996)pdf[22] H. Motoda and T. Washio, “Machine Learning and Data Mining, ” Journalof JSAI, Vol.12, No.4, pp.505-512 (1997) pdf[23] H. Motoda, ”Directions of Knowledge-Based Systems Research in the Era of
Personalization and Globalization of Information Processing (in Japanese), ”
J. of JSAI, Vo.11, No.3, pp.371-375 (1966) pdf[24] R. Mizoguchi and H. Motoda, “Expert Systems Research in Japan,” Invited,
IEEE Expert, Vol.10, No.4, August, pp.14-23 (1995)[25] H. Motoda, “Book Review: Second Generation Expert System, Edited by
Jean-Marc David, Jean-Paul Krivine and Raid Simmons,” Invited, IEEE Expert, Vol.9, No.2, April, pp.66-76 (1994)[26] H. Motoda, “Approach to Knowledge Reuse – Research Activities in KADS
related projects in Europe –,” Invited, in Japanese, J. of JSAI, Vol.9, No.1,
pp.10-16 (1994) pdf[27] H. Motoda, R. Mizoguchi and T. Nishida, ”Knowledge Sharing and Resuse,”
J. of JSAI, Vol.9, No.1, pp.2 (1994) pdf[28] H. Motoda, R. Mizoguchi, J. Boose and B. Gaines, “Knowledge Acquisition
for Knowledge-based Systems,” Invited, IEEE Expert, Vol. 6, No.4, pp.53-64(1991)[29] H. Motoda and Kenichi Yoshida, “Application of Qualitative Reasoning toKnowledge Acquisition,” in Japanese, Invited, J. of IPSJ., Vol.32, No.2, pp.153162 (1991) pdf[30] H. Motoda, “Current Status of Expert System Development and Related
Technologies in JAPAN,” Invited, IEEE Expert, Vol.5, No.4, pp.3-11, August (1990)[31] H. Motoda and K. Yoshida, “Qualitative Reasoning as a Basis of Deep Reasoning ,” in Japanese, Invited, J. of JSAI, Vol.4, No.5, pp.538-546 (1989) pdf[32] H. Motoda, ”Information Processing by Neural Network – Introduction toConnectionism or Towards Soft Symbols (H. Asoh),” Book Review in Japanese,J. of JSAI, Vol.4, No.4 , pp.470-471 (1989) pdf[33] H. Motoda, “Hurdle for Knowledge Information Processing – Expert System,”in Japanese, Invited, J. of IEICE, Vol.72, No.3, pp.265-271 (1989)[34] H. Motoda,“New technical trends of Machine Learning and Knowledge Acquisition,” in Japanese, Invited, J. of JSAI, Vol.3, No.6, pp.691-694 (1988)
pdf[35] H. Motoda, “On the Thresholds of Knowledge (D. B. Lenat and E. A. Feigen57

baum), “ Literature review in Japanese, J. of JSAI, Vol.3, No.1, pp.121-122(1988) pdf[36] M. Ishizuka, A. Koyama, S. Takagi, J. Tujii, H. Motoda, and H. Ueno, “Problems with Expert Systems,” in Japanese, Invited, Information Processing,Vol.28, No.2, pp.218-236 (1987) pdf[37] H. Motoda, T. Ogino, K. Sekimizu, Y. Shinohara and M. Kitamura,“ Knowledge Engineering and Nuclear Technology,” in Japanese, Invited, Journal of
Atomic Energy Society of Japan, Vol.28, No.9, pp.2-13 (1986) pdf[38] H. Motoda, “Application of Knowledge Information Processing to Plant Control,” in Japanese, Invited, Computol, Vol.10, pp.147-153 (1985)[39] H. Motoda, “Problem Solving by Knowledge,” in Japanese, Invited, Journalof Atomic Energy Society of Japan, Vol.26, No.4, pp.271-276 (1984) pdf[40] T. Kiguchi and H. Motoda, “Application of Knowledge Engineering to Nuclear
System,” in Japanese, Invited, Energy Review, Vol.1, pp.7-11 (1894)[41] Y. Kobayashi and H. Motoda, “Teknowledge that sells Knowledge Engineering”, in Japanese, Invited, J. of Mechanical Society of Japan, Vol.86, No.780,
pp.1300-1301 (1983) pdf[42] H. Motoda, T. Kiguchi and S. Kobayashi, “Application of Knowledge Engineering to Nuclear System,” in Japanese, Invited, Systems and Control,
Vol.27, No.7, pp.448-454 (1983)
4. Books[1] T. Cao, E. Lim, Z. Zhou, T. Bao, D. Cheung and H. Motoda, “Advanced in
Knowledge Discovery and Data Mining (Proceedings of PAKDD2015, Part 1),
LNAI 9077, Springer (2015)[2] T. Cao, E. Lim, Z. Zhou, T. Bao, D. Cheung and H. Motoda, “Advanced in
Knowledge Discovery and Data Mining (Proceedings of PAKDD2015, Part 2),
LNAI 9078, Springer (2015)[3] M. Kryszkiewicz, C. Cornelis, D. Ciucci, J. Medina-Moreno, H. Motoda andZ. W. Ras, “Rough Sets and Intelligent Systems Paradidigms - Proceedings of
the Second International Conference, RSEISP 2014”, held as part of JRS2014,
LNAI 8537, Springer (2014)[4] L. Cao, H. Motoda, J. Srivastava, E. Lim and I. King (Eds.), “Behavior andSocial Computing, (Proceedings of the International Workshop on Behavioral
and Social Informatics, BSI2013 (Gold Coast) and the International Workshop
on Behavior and Social Infromatics and Computing, BSIC2013 (Beijing)”,
Lecture Notes in Computer Science 8178, Springer (2013)[5] H. Motoda, Z. Wu, L. Cao, O. Zaiane, M. Yao and W. Wang (Eds.), “Advanced Data Mining and Applications, (Proceedings of ADMA 2013 - Part1)”, Lecture Notes in Artiﬁcial Intelligence 8346, Springer (2013)[6] H. Motoda, Z. Wu, L. Cao, O. Zaiane, M. Yao and W. Wang (Eds.), “Ad58vanced Data Mining and Applications, (Proceedings of ADMA 2013 - Part2)”, Lecture Notes in Artiﬁcial Intelligence 8347, Springer (2013)[7] J. Pei, V. S. Tseng, L. Cao, H. Motoda and G. Xu (Eds.), “Advances in
Knowledge Discovery and Data Mining, (Proceedings of PAKDD 2013 - Part1)”, Lecture Notes in Artiﬁcial Intelligence 7818, Springer (2013)[8] J. Pei, V. S. Tseng, L. Cao, H. Motoda and G. Xu (Eds.), “Advances in
Knowledge Discovery and Data Mining, (Proceedings of PAKDD 2013 - Part2)”, Lecture Notes in Artiﬁcial Intelligence 7819, Springer (2013)[9] H. Motoda, T. Kurita, T. Higuchi, Y. Matsumoto and N. Murata, “Pattern
Recognition and Machine Learning, Vol. 1 and 2”, Springer Japan (2007),
Translation of the C.M. Bishop’s Pattern Recognition and Machine Learning,
Springer (2006)[10] H. Liu and H. Motoda (Eds), “Computational Methods of Feature Selection,”
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series (2008)[11] H. Motoda, S. Tsumoto, Yamaguci, M. Numao,“Fundamentals of Data Mining” (in Japanese), Ohmsha (2007)[12] A. Hoﬀmann, H. Motoda and T. Scheﬀer (Eds.), “Discovery Science”, Lecture
Notes in Artiﬁcial Intelligence 3735, Springer (2005)[13] S. Tsumoto, T. Yamaguchi, M. NUmao and H. Motoda, “Active Mining”,
Lecture Notes in Artiﬁcial Intelligence 3430, Springer (2005)[14] H. Motoda (ed.), “Active Mining - New Directions of Data Mining -”, Frontiersin Artiﬁcial INtelligence and Applications, IOS Press (2002)[15] A. Suzuki and H. Motoda, “Design of Systems, Managament and Evaluation”,
Iwanami Modern Engineering Series (2001)[16] H. Liu and H. Motoda, “Instance Selection and Construction for Data Mining(Eds.)”, Kluwer Academic Publishers (2001)[17] P.Compton, A. Hoﬀmann, H. Motoda and T. Yamaguchi, “Proc. of the 6th
Paciﬁc Rim Knowledge Acquisition Workshop (PKAW2000) (2000)[18] S. Arikawa and H. Motoda (Eds), “Discovery Science”, Lecture Notes in Artiﬁcial Intelligence 1532, Springer (1998)[19] H. Motoda, R. Mizoguchi, P. Compton and H. Liu (Eds), “Paciﬁc Rim Knowledge Acquisition Workshop (PKAW98)” (1998)[20] H. Lee and H. Motoda, “PRICAI98: Topics in Artiﬁcial Intelligence”, Lecture
Notes in Artiﬁcial Intelligence 1531, Springer (1998)[21] H. Liu and H. Motoda, “Feature Selection for Knowledge Discovery and Data
Mining”, Kluwer Academic Publishers (1998)[22] H. Liu and H. Motoda, “Feature Extraction, Construction and Selection -Data
Mining Perspective -, (Eds.)”, Kluwer Academic Publishers (1998)[23] H. Lu, H. Motoda and H. Liu (Eds.), “KDD: Techniques and Applications”,

59World Scientiﬁc (1997)
5. Chapters in books[1] H. Motoda and K. Ohara, “Apriori”, X. Wu and V. Kumar (Eds.), The Top
Ten Algorithms in Data Mining, Chapman & Hall/CRC, pp.61-92 (2009) pdf[2] K. Fukata, T. Washio1, K. Yada and H. Motoda1, “A Method to Search ARX
Model Orders and Its Application to Sales Dynamics Analysis,” in Ohsama(Ed.) Data Mining for Design and Marketing, Taylor and Francis (2008) pdf[3] T. Washio and H. Motoda, “Communicaility Criteria of Law Equation discovery”, S. Džeroski and L. Todorovski (Eds.), Computational Discovery of
Scientiﬁc Knowledge, pp.98-119, LNAI 4660, Springer (2007) pdf[4] K. Ohara, P.C. Nguyen, A. Mogi, H. Motoda and T. Washio, “Constructing
Decision Tree Based on Chunkingless Graph-based Induction”, Mining Graph
Data, L. B. Holder and D. J. Cook (eds), Chap.8, pp.203-226, Wiley Interscience (2006)[5] A. Inokuchi, T. Washio and H. Motoda, “A General Framework for Mining
Frequent Subgraphs from Labeled Graphs, T. Washio, L. De Raedt and J. N.
Knok (Eds.), Advances in Mining Graphs, Trees and Sequences, IOS Press,
pp. 53-82 (2005).
[6] W. Geamsakul, T. Yoshida, K. Ohara, H. Motoda, H. Yokoi and K. Takabayashi, “Constructing a Decision Tree for Graph-Structured Data and its
Applications, T. Washio, L. De Raedt and J. N. Knok (Eds.), Advances in
Mining Graphs, Trees and Sequences, IOS Press. to appear (2005).
[7] T. Wada, T. Yoshida, H. Motoda and T. Washio, “Acquiring Knowledge from
Both Human Experts and Accumulated Data in an Unstable Environment,
Active Mining: New Directions of Data Mining, H. Motoda (Ed.), pp.217228, IOS Press (2002)[8] K. Fujiwara, T. Yoshida, H. Motoda and T. Washio, “Case Generation Method
for Constructing an RDR Knowledge Base, Active Mining: New Directions of
Data Mining, H. Motoda (Ed.), pp.205-215, IOS Press (2002)[9] T. Matsuda, T. Yoshida, H. Motoda and T. Washio, “Knowledge Discovery
from Structured Data by Beam-wise Graph-Based Induction, Active Mining:
New Directions of Data Mining, H. Motoda (Ed.), pp.115-125, IOS Press(2002)[10] H. Motoda, “Qualitative Physics,” in Japanese, Chapter 21 in The Handbookof Artiﬁcial Intelligence (Translation from The Handbook of Artiﬁcial Intelligence, Edited by A. Barr, P. R. Cohen and Ed. A. Feigenbaum, Addison
Wesley, 1989), Kyoritsu Publishing Co., p. 387-484 (1993)[11] H. Motoda, “Causal Understanding (in Japanese),” Chapter 3 in Handbookof Cognitive Science, Kyoritsu Publishing Co., pp.118-127 (1991)[12] H. Motoda, “Plant Diagnosis (in English),” AI Technology (Chapter 6), Ed.
by F. Mizoguchi, Ohmsha, pp.109-147 (1990)60

[13] H. Motoda, “Towards Understanding Physical Systems by Qualitative Reasoning,” in Japanese, Chapter 9 in Qualitative Reasoning, Kyoritsu PublishingCo., pp.267-287 (1989)[14] H. Motoda, “Operator Guidance of Nuclear Power Plant,” in Japanese, 6.4.4
in AI Handbook, pp.334-356 (1987)[15] H. Motoda, “Plant Diagnosis,” in Japanese, AI Technology (Chapter 6), Ed.
by F. Mizoguchi, Ohmsha, pp.91-117 (1986)[16] H. Tanaka, S. Muto, Y. Kobayashi, Y. Ueda and H. Motoda, “Application of
KE technology to Layout Planning of Transformer Substation”, in Japanese,in III-17 in Expert Systems (Newest tools and Applications), Nikkan Kogyo
Gijyutu Center (1986)[17] H. Motoda, “Core Fuel management,” in Computer Control of Nuclear Reactors (in Japanese), Ed. by N. Suda, Tokyo Denki Univ. Press (1971)
6. Others (Archive, Panel, Conference report, etc.)[1] K. Saito, M. Kimura, K. Ohara, and H. Motoda, “ Learning AsynchronousTime Information Diﬀusion Models and its Application to Behavioral Data
Analysis over Social Networks,” arXiv: 1204.4528 (2012) pdf[2] T. Shinoda and Y. Matsuo, “Seventeenth Student Forum Interview – Interview with Hiroshi Motoda – I have my own understanding even for complex
phenomena (in Japanese) ”, J. of JSAI, Vol.18, No.6, pp.770-772 (2003) pdf[3] H. Motoda, et.al., ”Report on the 15th International Conference on Machine
Learning and the 8th International Conference on Inductive Logic Programming, ” J. of JSAI, Vol. 13, No.6, pp.1013-1015 (1998) pdf[4] H. Motoda, ”Report on the 8th International Workshop on Knowledge Acquisition (KAW’94),” Conference report, J. of JSAI, Vol.9, No.3, pp.458-460(1994) pdf[5] H. Motoda, ”Report on International Workshop on Machine Intelligence 1993,”
Conference report, J. of JSAI, Vol.9, No.2, pp.318-320 (1994) pdf[6] H. Motoda, R. Mizoguchi and T. Nishida, ”Report on Knowledge Sharing
and Reuse Workshop,” Conference report, J. of JSAI, Vol.8, No.5, pp.666-671(1993) pdf[7] H. Motoda, ”Report on JKAW-92,” Conference report, J. of JSAI, Vol.8, No.4,
pp.520-522 (1993) pdf[8] H. Motoda, ”A dream that is easy to have but hard to realize,” Commentary,
J. of JSAI, Vol.7, No.2, pp.185 (1992) pdf[9] H. Motoda, ”Report on JKAW-90,” Conference report, J. of JSAI, Vol.6, No.4,
pp.603-606 (1991) pdf[10] H. Motoda, et.al., ”AI-Neuro- Fuzzy,” Panel, J. of JSAI, Vol.6, No.4, pp.482500 (1991) pdf

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[11] H. Motoda, ”Motoda Laboratory at Advanced Research Laboratory, Hitachi,Ltd.,” J. of JSAI, Vol.6, No.2, pp.281-281 (1991) pdf[12] H. Motoda, et.al., ”Report on AAAI-90,” Conference report, J. of JSAI, Vol.6,
No.1, pp.131-137 (1991) pdf[13] H. Motoda, et.al., ”Report on IJCAI-89,” Conference report, J. of JSAI, Vol.5,
No.1, pp.115-122 (1989) pdf

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