OpenRaiser/Intern-Atlas huggingface dataset:
Classification and visualization for high-dimensional data
Authors: Alfred Inselberg, Tova Avidan
Venue: KDD
Year: 2000

Article Free Access Share on Classification and visualization for high-dimensional data Authors: Alfred Inselberg San Diego SuperComputing Center & Multidimensional Graphs Ltd, Raanana, Israel and Department of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel San Diego SuperComputing Center & Multidimensional Graphs Ltd, Raanana, Israel and Department of Computer Science, Tel Aviv University, Tel Aviv 69978, IsraelView Profile , Tova Avidan Department of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel Department of Computer Science, Tel Aviv University, Tel Aviv 69978, IsraelView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 370–374https://doi.org/10.1145/347090.347170Online:01 August 2000Publication History 19citation964DownloadsMetricsTotal Citations19Total Downloads964Last 12 Months17Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Active learning using adaptive resampling
Authors: Vijay S. Iyengar, Chidanand Apté, Tong Zhang
Venue: KDD
Year: 2000

Classi cation modeling a.k.a. supervised learning is an extremely useful analytical technique for developing predictive and forecasting applications. The explosive growth in data warehousing and internet usage has made large amounts of data potentially available for developing classi cation models. For example, natural language text is widely available in many forms e.g., electronic mail, news articles, reports, and web page contents. Categorization of data is a common activity which can be automated to a large extent using supervised learning methods. Examples of this include routing of electronic mail, satellite image classi cation, and character recognition. However, these tasks require labeled data sets of su ciently high quality with adequate instances for training the predictive models. Much of the on-line data, particularly the unstructured variety e.g., text, is unlabeled. Labeling is usually a expensive manual process done by domain experts. Active learning is an approach to solving this problem and works by i d e n tifying a subset of the data that needs to be labeled and uses this subset to generate classi cation models. We present an active learning method that uses adaptive resampling in a natural way t o signi cantly reduce the size of the required labeled set and generates a classi cation model that achieves the high accuracies possible with current adaptive resampling methods.
Scaling up dynamic time warping for datamining applications
Authors: Eamonn J. Keogh, Michael J. Pazzani
Venue: KDD
Year: 2000

There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA). Our approach allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.
Feature selection in unsupervised learning via evolutionary search
Authors: YongSeog Kim, W. Nick Street, Filippo Menczer
Venue: KDD
Year: 2000

Feature subset selection is an important problem in knowl- edge discovery, not only for the insight gained from deter- mining relevant modeling variables but also for the improved understandability, scalability, and possibly, accuracy of the resulting models. In this paper we consider the problem of feature selection for unsupervised learning. A number of heuristic criteria can be used to estimate the quality of clusters built from a given featuresubset. Rather than combining such criteria, we use ELSA, an evolutionary lo- cal selection algorithm that maintains a diverse population of solutions that approximate the Pareto front in a multi- dimensional objectiv espace. Each evolved solution repre- sents a feature subset and a number of clusters; a standard K-means algorithm is applied to form the given n umber of clusters based on the selected features. Preliminary results on both real and synthetic data show promise in finding Pareto-optimal solutions through which we can identify the significant features and the correct number of clusters.
Genome scale prediction of protein functional class from sequence using data mining
Authors: Ross D. King, Andreas Karwath, Amanda Clare, Luc Dehaspe
Venue: KDD
Year: 2000

The ability to predict protein function from amino acid sequence is a central research goal of molecular biology. Such a capability would greatly aid the biological interpretation of the genomic data and accelerate its medical exploitation.
Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities
Authors: Brendan Kitts, David Freed, Martin Vrieze
Venue: KDD
Year: 2000

We develop a method for recommending products to customers with applications to both on-line and surface mail promotional offers. Our method differs from previous work in collaborative filtering [8] and imputation [18], in that we assume probabilities are conditionally independent. This assumption, which is also made in Naive Bayes [5], enables us to pre-compute probabilities and store them in main memory, enabling very fast performance on millions of customers. The algorithm supports a variety of tunable parameters so that the method can address different promotional objectives. We tested the algorithm at an on-line hardware retailer, with 17,400 customers divided randomly into control and experimental groups. In the experimental group, clickthrough increased by +40% (p<0.01), revenue by +38% (p<0.07), and units sold by +61% (p<0.01). By changing the algorithm’s parameter settings we found that these results could be improved even further. This work demonstrates the considerable potential of automated data mining for dramatically increasing the profitability of on and off-line retail promotions.
Unsupervised Bayesian visualization of high-dimensional data
Authors: Petri Kontkanen, Jussi Lahtinen, Petri Myllymäki, Henry Tirri
Venue: KDD
Year: 2000

We propose a data reduction method based on a probabilistic similarity framework where tw o vectors are considered similar if they lead to similar predictions. We sho w ho w this type of a probabilistic similarity metric can be de ned both in a supervised and unsupervised manner. As a concrete application of the suggested multidimensional scaling scheme, we describe how the method can be used for producing visual images of high-dimensional data, and give several examples of visualizations obtained by using the suggested scheme with probabilistic Bayesian net w ork models.
High performance data mining (tutorial PM-3)
Authors: Vipin Kumar, Mohammed J. Zaki
Venue: KDD
Year: 2000

Article Free Access Share on High performance data mining (tutorial PM-3) Authors: Vipin Kumar University of Minnesota University of MinnesotaView Profile , Mohammed Zaki Rensselaer Polytechnic Rensselaer PolytechnicView Profile Authors Info & Claims KDD '00: Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 309–425https://doi.org/10.1145/349093.349109Online:20 August 2000Publication History 3citation5,430DownloadsMetricsTotal Citations3Total Downloads5,430Last 12 Months13Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Multi-level organization and summarization of the discovered rules
Authors: Bing Liu, Minqing Hu, Wynne Hsu
Venue: KDD
Year: 2000

Many existing data mining techniques often produce a large number of rules, which make it very difficult for manual inspection of the rules to identify those interesting ones. This problem represents a major gap between the results of data mining and the understanding and use of the mining results. In this paper, we argue that the key problem is not with the large number of rules because if there are indeed many rules that exist in data, they should be discovered. The main problem is with our inability to organize, summarize and present the rules in such a way that they can be easily analyzed by the user. In this paper, we propose a technique to intuitively organize and summarize the discovered rules. With this organization, the discovered rules can be presented to the user in the way as we think and talk about knowledge in our daily lives. This organization also allows the user to view the discovered rules at different levels of details, and to focus his/her attention on those interes...
Targeting the right students using data mining
Authors: Yiming Ma, Bing Liu, Ching Kian Wong, Philip S. Yu, Shuik Ming Lee
Venue: KDD
Year: 2000

education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore. The application involves many data mining tasks. This paper focuses only on one task, namely, selecting students for remedial classes. Traditionally, a cut-off mark for each subject is used to select the weak students. That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject. In this paper, we show that this traditional method requires too many students to take part in the remedial classes. This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics. With the help of data mining, we are able to select the targeted students much more precisely.
Efficient clustering of high-dimensional data sets with application to reference matching
Authors: Andrew McCallum, Kamal Nigam, Lyle H. Ungar
Venue: KDD
Year: 2000

Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at once---for example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, high-dimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. U...
Small is beautiful: discovering the minimal set of unexpected patterns
Authors: Balaji Padmanabhan, Alexander Tuzhilin
Venue: KDD
Year: 2000

Article Small is beautiful: discovering the minimal set of unexpected patterns Share on Authors: Balaji Padmanabhan The Wharton School, University of Pennsylvania, 1310 Steinberg-Dietrich Hall, Philadelphia, PA The Wharton School, University of Pennsylvania, 1310 Steinberg-Dietrich Hall, Philadelphia, PAView Profile , Alexander Tuzhilin Stern School of Business, New York University, 44 West 4lt;supgt;thlt;/supgt; Street, New York, NY Stern School of Business, New York University, 44 West 4lt;supgt;thlt;/supgt; Street, New York, NYView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 54–63https://doi.org/10.1145/347090.347103Online:01 August 2000Publication History 100citation873DownloadsMetricsTotal Citations100Total Downloads873Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Discovery of multi-level rules and exceptions from a distributed database
Authors: Rónán Páircéir, Sally I. McClean, Bryan W. Scotney
Venue: KDD
Year: 2000

Article Discovery of multi-level rules and exceptions from a distributed database Share on Authors: Rónán Páircéir School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern IrelandView Profile , Sally McClean School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern IrelandView Profile , Bryan Scotney School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern Ireland School of Information and Software Engineering, Faculty of Informatics, University of Ulster, Cromore Road, Coleraine, BT52 1SA, Northern IrelandView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 523–532https://doi.org/10.1145/347090.347196Online:01 August 2000Publication History 4citation767DownloadsMetricsTotal Citations4Total Downloads767Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Towards scalable support vector machines using squashing
Authors: Dmitry Pavlov, Darya Chudova, Padhraic Smyth
Venue: KDD
Year: 2000

Support vector machines (SVMs) provide classi cation models with strong theoretical foundations as well as excellent empirical performance on a variety of applications. One of the major drawbacks of SVMs is the necessity to solve a large-scale quadratic programming problem. This paper combines likelihood-based squashing with a probabilistic formulation of SVMs, enabling fast training on squashed data sets. We reduce the problem of training the SVMs on the weighted \squashed" data to a quadratic programming problem and show that it can be solved using Platt's sequential minimal optimization (SMO) algorithm. W e compare performance of the SMO algorithm on the squashed and the full data, as well as on simple random and boosted samples of the data. Experiments on a number of datasets show that squashing allows one to speed-up training, decrease memory requirements, and obtain parameter estimates close to that of the full data. More importantly, squashing produces close to optimal classi cation accuracies.
Can we push more constraints into frequent pattern mining?
Authors: Jian Pei, Jiawei Han
Venue: KDD
Year: 2000

Article Can we push more constraints into frequent pattern mining? Share on Authors: Jian Pei School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada V5A 1S6 School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada V5A 1S6View Profile , Jiawei Han School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada V5A 1S6 School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada V5A 1S6View Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 350–354https://doi.org/10.1145/347090.347166Online:01 August 2000Publication History 120citation852DownloadsMetricsTotal Citations120Total Downloads852Last 12 Months13Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Defection detection: using activity profiles to predict ISP customer vulnerability
Authors: Nandini Raghavan, Robert M. Bell, Matthias Schonlau
Venue: KDD
Year: 2000

Article Free Access Share on Defection detection: using activity profiles to predict ISP customer vulnerability Authors: Nandini Raghavan DoubleClick R&D, 450, W. 33rd Street, New York, NY DoubleClick R&D, 450, W. 33rd Street, New York, NYView Profile , Robert M. Bell AT&T Labs Research, 180 Park Ave, P.O. Box 971, Florham Park, NJ AT&T Labs Research, 180 Park Ave, P.O. Box 971, Florham Park, NJView Profile , Matthias Schonlau RAND, P.O. Box 2138, Santa Monica, CA RAND, P.O. Box 2138, Santa Monica, CAView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 506–515https://doi.org/10.1145/347090.347193Published:01 August 2000Publication History 3citation344DownloadsMetricsTotal Citations3Total Downloads344Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
A sequential sampling algorithm for a general class of utility criteria
Authors: Tobias Scheffer, Stefan Wrobel
Venue: KDD
Year: 2000

Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-best hypothesis problems where the goal is to nd the n hypotheses from a given hypothesis space that score best according to a given utility function. We present a sampling algorithm that solves this problem by issuing a small number of database queries while guaranteeing precise bounds on condence and quality of solutions. Known sampling algorithms assume that the utility be the average (over the examples) of some function, which is not the case for many frequently used utility functions. We show that our algorithm works for all utilities that can be estimated with bounded error. We provide such error bounds and resulting worst-case sample bounds for some of the most frequently used utilities, and prove that there is no sampling algorithm for another popular class of utility functions. The algorithm is sequential in the sense that it starts to return (or discard) hypotheses that already...
Ongoing management and application of discovered knowledge in a large regulatory organization: a case study of the use and impact of NASD Regulation's Advanced Detection System (RADS)
Authors: Ted E. Senator
Venue: KDD
Year: 2000

This paper describes a case study of an ongoing, deployed, KDD application -NASD Regulation's Advanced Detection System (ADS), which monitors activity on the Nasdaq Stock Marketfrom the perspective of how organizations and systems are impacted by the continuous long-term use of KDD in a dynamic domain. It discusses knowledge management techniques that have been developed for and applied to discovered knowledge. It points out special considerations resulting from the regulatory environment as well as general lessons applicable to all organizations that may come to rely on KDD as an integral part of their business operations. It describes our modifications to accepted KDD processes that are needed to account for the longterm use of KDD as part of a dynamic business environment.
An empirical analysis of techniques for constructing and searching k-dimensional trees
Authors: Douglas A. Talbert, Douglas H. Fisher
Venue: KDD
Year: 2000

Affordable, fast computers with large memories have lessened the demand for program efficiency, but applications such as browsing and searching very large databases often have ratelimiting constraints and therefore benefit greatly from improvements in efficiency. This paper empirically evaluates several variants of a common k-dimensional tree technique to demonstrate how different algorithm options influence search cost for nearest neighbors.
Textual data mining of service center call records
Authors: Pang-Ning Tan, Hannah Blau, Steven A. Harp, Robert P. Goldman
Venue: KDD
Year: 2000

Article Free Access Share on Textual data mining of service center call records Authors: Pang-Ning Tan Department of Computer Science, University of Minnesota, Minneapolis, MN Department of Computer Science, University of Minnesota, Minneapolis, MNView Profile , Hannah Blau Department of Computer Science, University of Massachusetts, Amherst, MA Department of Computer Science, University of Massachusetts, Amherst, MAView Profile , Steve Harp Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MNView Profile , Robert Goldman Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MNView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000Pages 417–423https://doi.org/10.1145/347090.347177Published:01 August 2000Publication History 26citation1,000DownloadsMetricsTotal Citations26Total Downloads1,000Last 12 Months33Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF