Technical Papers

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  Khaled Alsabti, Sanjay Ranka, Vineet Singh: CLOUDS: A Decision Tree Classifier for Large Datasets. 2-8
• 
  Paul S. Bradley, Usama M. Fayyad, Cory Reina: Scaling Clustering Algorithms to Large Databases. 9-15
• 
  Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, Padhraic Smyth: Rule Discovery from Time Series. 16-22
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  Gautam Das, Heikki Mannila, Pirjo Ronkainen: Similarity of Attributes by External Probes. 23-29
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  Luc Dehaspe, Hannu Toivonen, Ross D. King: Finding Frequent Substructures in Chemical Compounds. 30-36
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  Pedro Domingos: Occam's Two Razors: The Sharp and the Blunt. 37-43
• 
  Martin Ester, Alexander Frommelt, Hans-Peter Kriegel, Jörg Sander: Algorithms for Characterization and Trend Detection in Spatial Databases. 44-50
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  Valery Guralnik, Duminda Wijesekera, Jaideep Srivastava: Pattern Directed Mining of Sequence Data. 51-57
• 
  Alexander Hinneburg, Daniel A. Keim: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. 58-65
• 
  Wenke Lee, Salvatore J. Stolfo, Kui W. Mok: Mining Audit Data to Build Intrusion Detection Models. 66-72
• 
  Charles X. Ling, Chenghui Li: Data Mining for Direct Marketing: Problems and Solutions. 73-79
• 
  Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining. 80-86
• 
  Gholamreza Nakhaeizadeh, Charles Taylor, Carsten Lanquillon: Evaluating Usefulness for Dynamic Classification. 87-93
• 
  Balaji Padmanabhan, Alexander Tuzhilin: A Belief-Driven Method for Discovering Unexpected Patterns. 94-100
• 
  Greg Ridgeway, David Madigan, Thomas Richardson, John O'Kane: Interpretable Boosted Naïve Bayes Classification. 101-104
• 
  Martin Staudt, Jörg-Uwe Kietz, Ulrich Reimer: A Data Mining Support Environment and its Application on Insurance Data. 105-111
• 
  Evan W. Steeg, Derek A. Robinson, Ed Willis: Coincidence Detection: A Fast Method for Discovering Higher-Order Correlations in Multidimensional Data. 112-120
• 
  Ke Wang, Soon Hock William Tay, Bing Liu: Interestingness-Based Interval Merger for Numeric Association Rules. 121-128

Poster Papers

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  Charu C. Aggarwal, Zheng Sun, Philip S. Yu: Online Generation of Profile Association Rules. 129-133
• 
  Brigham S. Anderson, Andrew W. Moore: ADtrees for Fast Counting and for Fast Learning of Association Rules. 134-138
• 
  Stefan Berchtold, H. V. Jagadish, Kenneth A. Ross: Independence Diagrams: A Technique for Visual Data Mining. 139-143
• 
  Siddhartha Bhattacharyya: Direct Marketing Response Models Using Genetic Algorithms. 144-148
• 
  José Borges, Mark Levene: Mining Association Rules in Hypertext Databases. 149-153
• 
  John V. Carlis, Elizabeth Shoop, Scott Krieger: Blurring the Distinction between Command and Data in Scientific KDD. 154-158
• 
  Ernest P. Chan, Santiago Garcia, Salim Roukos: Probabilistic Modeling for Information Retrieval with Unsupervised Training Data. 159-163
• 
  Philip K. Chan, Salvatore J. Stolfo: Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection. 164-168
• 
  William W. Cohen, Haym Hirsh: Joins that Generalize: Text Classification Using WHIRL. 169-173
• 
  Corinna Cortes, Daryl Pregibon: Giga-Mining. 174-178
• 
  Anne Debregeas, Georges Hébrail: Interactive Interpretation of Kohonen Maps Applied to Curves. 179-183
• 
  Carmel Domshlak, D. Gershkovich, Ehud Gudes, N. Liusternik, Amnon Meisels, Tzachi Rosen, Solomon Eyal Shimony: FlexiMine - A Flexible Platform for KDD Research and Application Construction. 184-188
• 
  William DuMouchel, Matthias Schonlau: A Fast Computer Intrusion Detection Algorithm Based on Hypothesis Testing of Command Transition Probabilities. 189-193
• 
  Usama M. Fayyad, Cory Reina, Paul S. Bradley: Initialization of Iterative Refinement Clustering Algorithms. 194-198
• 
  A. J. Feelders, Soong Chang, Geoffrey J. McLachlan: Mining in the Presence of Selectivity Bias and its Application to Reject Inference. 199-203
• 
  Goetz Graefe, Usama M. Fayyad, Surajit Chaudhuri: On the Efficient Gathering of Sufficient Statistics for Classification from Large SQL Databases. 204-208
• 
  Dan L. Grecu, Lee A. Becker: Coactive Learning for Distributed Data Mining. 209-213
• 
  Jiawei Han, Wan Gong, Yiwen Yin: Mining Segment-Wise Periodic Patterns in Time-Related Databases. 214-218
• 
  Simon Handley, Pat Langley, Folke A. Rauscher: Learning to Predict the Duration of an Automobile Trip. 219-223
• 
  Theodore Johnson, Ivy Kwok, Raymond T. Ng: Fast Computation of 2-Dimensional Depth Contours. 224-228
• 
  Theodore Johnson, Tamraparni Dasu: Comparing Massive High-Dimensional Data Sets. 229-233
• 
  Mark G. Kelly, David J. Hand, Niall M. Adams: Defining the Goals to Optimise Data Mining Performance. 234-238
• 
  Eamonn J. Keogh, Michael J. Pazzani: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. 239-243
• 
  Randy Kerber, Hal Beck, Tej Anand, Bill Smart: Active Templates: Comprehensive Support for the Knowledge Discovery Process. 244-248
• 
  Ron Kohavi, Dan Sommerfield: Targeting Business Users with Decision Table Classifiers. 249-253
• 
  Petri Kontkanen, Petri Myllymäki, Tomi Silander, Henry Tirri: BAYDA: Software for Bayesian Classification and Feature Selection. 254-258
• 
  Terran Lane, Carla E. Brodley: Approaches to Online Learning and Concept Drift for User Identification in Computer Security. 259-263
• 
  Sofus A. Macskassy, Arunava Banerjee, Brian D. Davison, Haym Hirsh: Human Performance on Clustering Web Pages: A Preliminary Study. 264-268
• 
  Sally I. McClean, Bryan W. Scotney, Mary Shapcott: Aggregation of Imprecise and Uncertain Information for Knowledge Discovery in Databases. 269-273
• 
  Nimrod Megiddo, Ramakrishnan Srikant: Discovering Predictive Association Rules. 274-278
• 
  John E. Moody, Matthew Saffell: Reinforcement Learning for Trading Systems and Portfolios. 279-283
• 
  Tadeusz Morzy, Maciej Zakrzewicz: Group Bitmap Index: A Structure for Association Rules Retrieval. 284-288
• 
  Gholamreza Nakhaeizadeh, Alexander Schnabl: Towards the Personalization of Algorithms Evaluation in Data Mining. 289-293
• 
  Tim Oates, David Jensen: Large Datasets Lead to Overly Complex Models: An Explanation and a Solution. 294-298
• 
  Jonathan J. Oliver, Ted Roush, Paul Gazis, Wray L. Buntine, Rohan A. Baxter, Steven R. Waterhouse: Analysing Rock Samples for the Mars Lander. 299-303
• 
  Srinivasan Parthasarathy, Mohammed Javeed Zaki, Wei Li: Memory Placement Techniques for Parallel Association Mining. 304-308
• 
  José C. Pinheiro, Don X. Sun: Methods for Linking and Mining Massive Heterogeneous Databases. 309-313
• 
  Andreas L. Prodromidis, Salvatore J. Stolfo: Mining Databases with Different Schemas: Integrating Incompatible Classifiers. 314-318
• 
  R. Bharat Rao, Scott Rickard, Frans Coetzee: Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers. 319-323
• 
  Saharon Rosset: Ranking - Methods for Flexible Evaluation and Efficient Comparison of Classification Performance. 324-328
• 
  Lisa Singh, Bin Chen, Rebecca Haight, Peter Scheuermann, Kiyoko Aoki: A Robust System Architecture for Mining Semi-Structured Data. 329-333
• 
  Ramesh Subramonian: Defining diff as a Data Mining Primitive. 334-338
• 
  Einoshin Suzuki: Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases. 339-343
• 
  Shiby Thomas, Sunita Sarawagi: Mining Generalized Association Rules and Sequential Patterns Using SQL Queries. 344-348
• 
  Hui Wang, Ivo Düntsch, David A. Bell: Data Reduction Based on Hyper Relations. 349-353
• 
  Andreas S. Weigend, Fei Chen, Stephen Figlewski, Steven R. Waterhouse: Discovering Technical Traders in the T-bond Futures Market. 354-358
• 
  Gary M. Weiss, Haym Hirsh: Learning to Predict Rare Events in Event Sequences. 359-363
• 
  Beat Wüthrich, D. Permunetilleke, S. Leung, Vincent Cho, Jian Zhang, W. Lam: Daily Prediction of Major Stock Indices from Textual WWW Data. 364-368
• 
  Mohammed Javeed Zaki, Neal Lesh, Mitsunori Ogihara: PlanMine: Sequence Mining for Plan Failures. 369-374

Conference Report

• 
  Arnold Goodman, John F. Elder IV: Knowledge Discovery and the Interface of Computing and Statistics. 375-378

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