Table of Contents

• Sharing Learned Models among Remote Database Partitions by Local Meta-LearningPhilip K. Chan, Salvatore J. Stolfo. 2-7
• Combining Data Mining and Machine Learning for Effective User ProfilingTom Fawcett, Foster J. Provost. 8-13
• Local Induction of Decision Trees: Towards Interactive Data MiningTruxton Fulton, Simon Kasif, Steven Salzberg, David L. Waltz. 14-19
• Knowledge Discovery in RNA Sequence Families of HIV Using Scalable ComputersIvo L. Hofacker, Martijn A. Huynen, Peter F. Stadler, Paul E. Stolorz. 20-25
• Parallel Halo Finding in N-Body Cosmology SimulationsDavid W. Pfitzner, John K. Salmon. 26-31
• Scalable Exploratory Data Mining of Distributed Geoscientific DataEddie C. Shek, Richard R. Muntz, Edmond Mesrobian, Kenneth W. Ng. 32-37
• Using a Hybrid Neural/Expert System for Data Base Mining in Market Survey DataVictor Ciesielski, Gregory Palstra. 38-43
• Discovering Knowledge in Commercial Databases Using Modern Heuristic TechniquesBeatriz de la Iglesia, Justin C. W. Debuse, Victor J. Rayward-Smith. 44-49
• KDD for Science Data Analysis: Issues and ExamplesUsama M. Fayyad, David Haussler, Paul E. Stolorz. 50-56
• Data Mining and Model Simplicity: A Case Study in DiagnosisGregory M. Provan, Moninder Singh. 57-62
• Automated Discovery of Medical Expert System Rules from Clinical Databases Based on Rough SetsShusaku Tsumoto, Hiroshi Tanaka. 63-69
• Automated Discovery of Active Motifs in Multiple RNA Secondary StructuresJason Tsong-Li Wang, Bruce A. Shapiro, Dennis Shasha, Kaizhong Zhang, Chia-Yo Chang. 70-75
• Detecting Early Indicator Cars in an Automotive Database: A Multi-Strategy ApproachRüdiger Wirth, Thomas P. Reinartz. 76-81
• Knowledge Discovery and Data Mining: Towards a Unifying FrameworkUsama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth. 82-88
• An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery ApplicationsGregory Piatetsky-Shapiro, Ronald J. Brachman, Tom Khabaza, Willi Klösgen, Evangelos Simoudis. 89-95
• Linear-Time Rule InductionPedro Domingos. 96-101
• Learning from Biased Data Using Mixture ModelsA. J. Feelders. 102-107
• Discovery of Relevant New Features by Generating Non-Linear Decision TreesAndreas Ittner, Michael Schlosser. 108-113
• Error-Based and Entropy-Based Discretization of Continuous FeaturesRon Kohavi, Mehran Sahami. 114-119
• Rethinking the Learning of Belief Network ProbabilitiesRon Musick. 120-125
• Clustering Using Monte Carlo Cross-ValidationPadhraic Smyth. 126-133
• Harnessing Graphical Structure in Markov Chain Monte Carlo LearningPaul E. Stolorz, Philip C. Chew. 134-139
• Imputation of Missing Data Using Machine Learning TechniquesKamakshi Lakshminarayan, Steven A. Harp, Robert P. Goldman, Tariq Samad. 140-145
• Discovering Generalized Episodes Using Minimal OccurrencesHeikki Mannila, Hannu Toivonen. 146-151
• Metapattern Generation for Integrated Data MiningWei-Min Shen, Bing Leng. 152-157
• Automated Pattern Mining with a Scale DimensionJan M. Zytkow, Robert Zembowicz. 158-163
• A Linear Method for Deviation Detection in Large DatabasesAndreas Arning, Rakesh Agrawal, Prabhakar Raghavan. 164-169
• Planning Tasks for Knowledge Discovery in Databases; Performing Task-Oriented User-GuidanceRobert Engels. 170-175
• Predictive Data Mining with Finite MixturesPetri Kontkanen, Petri Myllymäki, Henry Tirri. 176-182
• An Empirical Test of the Weighted Effect Approach to Generalized Prediction Using Recursive Neural NetsRense Lange. 183-188
• Multiple Uses of Frequent Sets and Condensed Representations (Extended Abstract)Heikki Mannila, Hannu Toivonen. 189-194
• A Comparison of Approaches for Maximizing Business Payoff of Prediction ModelsBrij M. Masand, Gregory Piatetsky-Shapiro. 195-201
• Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree HybridRon Kohavi. 202-207
• Quakefinder: A Scalable Data Mining System for Detecting Earthquakes from SpacePaul E. Stolorz, Christopher Dean. 208-213
• Extensibility in Data Mining SystemsStefan Wrobel, Dietrich Wettschereck, Edgar Sommer, Werner Emde. 214-219
• Mining Knowledge in Noisy Audio DataAndrzej Czyzewski. 220-225
• A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with NoiseMartin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu. 226-231
• A Method for Reasoning with Structured and Continuous Attributes in the INLEN-2 Multistrategy Knowledge Discovery SystemKenneth A. Kaufman, Ryszard S. Michalski. 232-237
• Self-Organizing Maps of Document Collections: A New Approach to Interactive ExplorationKrista Lagus, Timo Honkela, Samuel Kaski, Teuvo Kohonen. 238-243
• The Quest Data Mining SystemRakesh Agrawal, Manish Mehta 0002, John C. Shafer, Ramakrishnan Srikant, Andreas Arning, Toni Bollinger. 244-249
• DBMiner: A System for Mining Knowledge in Large Relational DatabasesJiawei Han, Yongjian Fu, Wei Wang, Jenny Chiang, Wan Gong, Krzysztof Koperski, Deyi Li, Yijun Lu, Amynmohamed Rajan, Nebojsa Stefanovic, Betty Xia, Osmar R. Zaïane. 250-255
• DataMine: Application Programming Interface and Query Language for Database MiningTomasz Imielinski, Aashu Virmani, Amin Abdulghani. 256-262
• Evaluating the Interestingness of Characteristic RulesMicheline Kamber, Rajjan Shinghal. 263-266
• Discovering Classification Knowledge in Databases Using Rough SetsNing Shan, Wojciech Ziarko, Howard J. Hamilton, Nick Cercone. 271-274
• Exceptional Knowledge Discovery in Databases Based on Information TheoryEinoshin Suzuki, Masamichi Shimura. 275-278
• Interactive Knowledge Discovery from Marketing Questionnaire Using Simulated Breeding and Inductive Learning MethodsTakao Terano, Yoko Ishino. 279-282
• Representing Discovered Patterns Using Attributed HypergraphYang Wang, Andrew K. C. Wong. 283-286
• Developing Tightly-Coupled Data Mining Applications on a Relational Database SystemRakesh Agrawal, Kyuseok Shim. 287-290
• Mining Entity-Identification Rules for Database IntegrationM. Ganesh, Jaideep Srivastava, Travis Richardson. 291-294
• A Genetic Algorithm-Based Approach to Data MiningIan W. Flockhart, Nicholas J. Radcliffe. 299-302
• Deriving Queries from Results Using Genetic ProgrammingTae-Wan Ryu, Christoph F. Eick. 303-306
• Maintenance of Discovered Knowledge: A Case in Multi-Level Association RulesDavid Wai-Lok Cheung, Vincent T. Y. Ng, Benjamin W. Tam. 307-310
• Analysing Binary AssociationsArno J. Knobbe, Pieter W. Adriaans. 311-314
• Growing Simpler Decision Trees to Facilitate Knowledge DiscoveryKevin J. Cherkauer, Jude W. Shavlik. 315-318
• Data Mining and Tree-Based OptimizationRobert L. Grossman, Haim Bodek, Dave Northcutt, Vince Poor. 323-326
• Induction of Condensed DeterminationsPat Langley. 327-330
• SE-Trees Outperform Decision Trees in Noisy DomainsRon Rymon. 331-334
• Learning Limited Dependence Bayesian ClassifiersMehran Sahami. 335-338
• RITIO - Rule Induction Two In OneDavid Urpani, Xindong Wu, Jim Sykes. 339-342
• Mining Associations in Text in the Presence of Background KnowledgeRonen Feldman, Haym Hirsh. 343-346
• Extraction of Spatial Proximity Patterns by Concept GeneralizationEdwin M. Knorr, Raymond T. Ng. 347-350
• Pattern Discovery in Temporal Databases: A Temporal Logic ApproachBalaji Padmanabhan, Alexander Tuzhilin. 351-354
• Exploiting Background Knowledge in Automated DiscoveryJohn M. Aronis, Foster J. Provost, Bruce G. Buchanan. 355-358
• Inferring Hierarchical Clustering Structures by Deterministic AnnealingThomas Hofmann, Joachim M. Buhmann. 363-366
• Static Versus Dynamic Sampling for Data MiningGeorge H. John, Pat Langley. 367-370
• Efficient Search for Strong Partial DeterminationsStefan Kramer, Bernhard Pfahringer. 371-374
• Reverse Engineering Databases for Knowledge DiscoveryStephen McKearney, Huw Roberts. 375-378
• Performing Effective Feature Selection by Investigating the Deep Structure of the DataMarco Richeldi, Pier Luca Lanzi. 379-383
• Efficient Implementation of Data Cubes Via Materialized ViewsJeffrey D. Ullman. 386-388