Data Mining and Knowledge Discovery via Logic-Based Methods [electronic resource] : Theory, Algorithms, and Applications /
Triantaphyllou, Evangelos.author.
The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human activity and interest. While numerous methods have been developed, the focus of this book presents algorithms and applications using one popular method that has been formulated in terms of binary attributes, i.e., by Boolean functions defined on several attributes that are easily transformed into rules that can express new knowledge. This book presents methods that deal with key data mining and knowledge discovery issues in an intuitive manner, in a natural sequence, and in a way that can be easily understood and interpreted by a wide array of experts and end users. The presentation provides a unique perspective into the essence of some fundamental DM issues, many of which come from important real life applications such as breast cancer diagnosis. Applications and algorithms are accompanied by extensive experimental results and are presented in a way such that anyone with a minimum background in mathematics and computer science can benefit from the exposition. Rigor in mathematics and algorithmic development is not compromised and each chapter systematically offers some possible extensions for future research.
Description based upon print version of record.
Includes bibliographical references (p. [317]-333) and indexes.
Algorithmic Issues -- Inferring a Boolean Function from Positive and Negative Examples -- A Revised Branch-and-Bound Approach for Inferring a Boolean Function from Examples -- Some Fast Heuristics for Inferring a Boolean Function from Examples -- An Approach to Guided Learning of Boolean Functions -- An Incremental Learning Algorithm for Inferring Boolean Functions -- A Duality Relationship Between Boolean Functions in CNF and DNF Derivable from the Same Training Examples -- The Rejectability Graph of Two Sets of Examples -- Application Issues -- The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosis -- Data Mining and Knowledge Discovery by Means of Monotone Boolean Functions -- Some Application Issues of Monotone Boolean Functions -- Mining of Association Rules -- Data Mining of Text Documents -- First Case Study: Predicting Muscle Fatigue from EMG Signals -- Second Case Study: Inference of Diagnostic Rules for Breast Cancer -- A Fuzzy Logic Approach to Attribute Formalization: Analysis of Lobulation for Breast Cancer Diagnosis -- Conclusions.
The importance of having efficient and effective methods for data mining and knowledge discovery (DM) is rapidly growing. This is due to the wide use of fast and affordable computing power and data storage media and also the gathering of huge amounts of data in almost all aspects of human activity and interest. While numerous methods have been developed, the focus of this book presents algorithms and applications using one popular method that has been formulated in terms of binary attributes, i.e., by Boolean functions defined on several attributes that are easily transformed into rules that can express new knowledge. This book presents methods that deal with key data mining and knowledge discovery issues in an intuitive manner, in a natural sequence, and in a way that can be easily understood and interpreted by a wide array of experts and end users. The presentation provides a unique perspective into the essence of some fundamental DM issues, many of which come from important real life applications such as breast cancer diagnosis. Applications and algorithms are accompanied by extensive experimental results and are presented in a way such that anyone with a minimum background in mathematics and computer science can benefit from the exposition. Rigor in mathematics and algorithmic development is not compromised and each chapter systematically offers some possible extensions for future research.
Data mining.
Computer logic.
Mathematical logic.
Operations research.
Management science.
Decision making.
Data Mining and Knowledge Discovery.
Logics and Meanings of Programs.
Mathematical Logic and Foundations.
Operations Research, Management Science.
Mathematical Logic and Formal Languages.
Operations Research/Decision Theory.
Springer optimization and its applications