Fourth International Conference on Knowledge Discovery and Data Mining
Sponsored by the American Association for Artificial Intelligence
Rakesh Agrawal and Paul Stolorz; Gregory Piatetsky-Shapiro, General Chair
Published by The AAAI Press, Menlo Park, California. This proceedings is also available in book and CD format.
Alsabti, Khaled; Ranka, Sanjay; Singh, Vineet 1998. CLOUDS: A Decision Tree Classifier for Large Datasets. In Proceedings of the Fourth Knowledge Discovery and Data Mining Conference, eds. Rakesh Agrawal and Paul Stolorz, 2-8. Menlo Park, Calif.: AAAI Press.
Please Note: Abstracts are linked to individual titles, and will appear in a separate browser window. Full-text versions of the papers are linked to the abstract text. Access to full text may be restricted.
Contents
A PDF version of this file is also available
Corporate Sponsors / xi
Preface / xii
Technical Papers
CLOUDS: A Decision Tree Classifier for Large Datasets / 2
Khaled Alsabti, Syracuse University; Sanjay Ranka, University of Florida; Vineet Singh, Hitachi America, Ltd.
Scaling Clustering
Algorithms to Large Databases / 9
P. S. Bradley, Usama Fayyad and Cory Reina, Microsoft Research
Rule Discovery from
Time Series / 16
Gautam Das and King-Ip Lin, University of Memphis; Heikki Mannila, University of Helsinki; Gopal Renganathan,
Autozone Inc.; Padhraic Smyth, University of California, Irvine
Similarity of Attributes
by External Probes / 23
Gautam Das, University of Memphis; Heikki Mannila and Pirjo Ronkainen, University of Helsinki
Finding Frequent
Substructures in Chemical Compounds / 30
Luc Dehaspe, Katholieke Universiteit Leuven; Hannu Toivonen, University of Helsinki; Ross Donald King,
The University of Wales, Aberystwyth
Occam’s Two Razors:
The Sharp and the Blunt / 37
Pedro Domingos, Instituto Superior Técnico
Algorithms for Characterization
and Trend Detection in Spatial Databases / 44
Martin Ester, Alexander Frommelt, Hans-Peter Kriegel and Jörg Sander, University of Munich
Pattern Directed Mining
of Sequence Data / 51
Valery Guralnik, Duminda Wijesekera and Jaideep Srivastava, University of Minnesota
An Efficient
Approach to Clustering in Large Multimedia Databases with Noise
/ 58
Alexander Hinneburg and Daniel A. Keim, University of Halle
Mining Audit Data to
Build Intrusion Detection Models / 66
Wenke Lee, Salvatore J. Stolfo and Kui W. Mok, Columbia University
Data Mining for Direct
Marketing: Problems and Solutions / 73
Charles X. Ling and Chenghui Li, The University of Western Ontario
Integrating Classification
and Association Rule Mining / 80
Bing Liu, Wynne Hsu and Yiming Ma, National University of Singapore
Evaluating
Usefulness for Dynamic Classification / 87
Gholamreza Nakhaeizadeh, Daimler-Benz Research and Technology; Charles Taylor, University of Leeds; Carsten Lanquillon,
Daimler-Benz Research and Technology
A Belief-Driven
Method for Discovering Unexpected Patterns / 94
Balaji Padmanabhan, Leonard N. Stern School of Business and Alexander Tuzhilin, Columbia University
Interpretable
Boosted Naïve Bayes Classification / 101
Greg Ridgeway, David Madigan, Thomas Richardson and John O'Kane, University of Washington
A Data Mining Support
Environment and its Application on Insurance Data / 105
M. Staudt, J.-U. Kietz and U. Reimer, Swiss Life
Coincidence Detection:
A Fast Method for Discovering Higher-Order Correlations in
Multidimensional Data / 112
Evan W. Steeg, Derek A. Robinson and Ed Willis, Molecular Mining Corporation
Interestingness-Based
Interval Merger for Numeric Association Rules / 121
Ke Wang, Soon Hock William Tay and Bing Liu, National University of Singapore
Poster Papers
Online Generation
of Profile Association Rules / 129
Charu C. Aggarwal, T. J. Watson Research Center; Zheng Sun, Duke University; Philip S. Yu, T. J. Watson Research Center
ADtrees for Fast Counting
and for Fast Learning of Association Rules / 134
Brigham Anderson and Andrew Moore, Carnegie Mellon University
Independence Diagrams:
A Technique for Visual Data Mining / 139
Stefan Berchtold and H. V. Jagadish, AT&T Laboratories; Kenneth A. Ross, Columbia University
Direct Marketing Response
Models Using Genetic Algorithms / 144
Siddhartha Bhattacharyya, University of Illinois at Chicago
Mining Association
Rules in Hypertext Databases / 149
José Borges and Mark Levene, University College London
Blurring the Distinction
between Command and Data in Scientific KDD / 154
John Carlis, Elizabeth Shoop and Scott Krieger, University of Minnesota
Probabilistic Modeling
for Information Retrieval with Unsupervised Training Data /
159
Ernest P. Chan, Credit Suisse First Boston; Santiago Garcia, Morgan Stanley & Co.; Salim Roukos, IBM T. J. Watson Research Center
Toward Scalable Learning
with Non-Uniform Class and Cost Distributions:
A Case Study in Credit Card Fraud Detection / 164
Philip K. Chan, Florida Institute of Technology and Salvatore J. Stolfo, Columbia University
Joins that Generalize:
Text Classification Using WHIRL / 169
William W. Cohen, AT&T Labs–Research and Haym Hirsh, Rutgers University
Giga-Mining
/ 174
Corinna Cortes and Daryl Pregibon, AT&T Labs–Research
Interactive Interpretation
of Kohonen Maps Applied to Curves / 179
Anne Debregeas and Georges Hebrail, Electricite de France
FlexiMine–A Flexible
Platform for KDD Research and Application Construction / 184
C. Domshlak, D. Gershkovich, E. Gudes, N. Liusternik, A. Meisels, T. Rosen and S. E. Shimony, Ben-Gurion University
A Fast Computer Intrusion
Detection Algorithm Based on Hypothesis Testing of Command
Transition Probabilities / 189
William DuMouchel, AT&T Labs–Research and Matthias Schonlau, AT&T Labs–Research and National Institute of Statistical Sciences
Initialization of
Iterative Refinement Clustering Algorithms / 194
Usama Fayyad, Cory Reina and P. S. Bradley, Microsoft Research
Mining in the Presence
of Selectivity Bias and its Application to Reject Inference
/ 199
A. J. Feelders, Tilburg University; Soong Chang and G. J. McLachlan, University of Queensland
On the Efficient
Gathering of Sufficient Statistics for Classification from Large
SQL Databases / 204
Goetz Graefe, Usama Fayyad and Surajit Chaudhuri, Microsoft Corporation
Coactive Learning
for Distributed Data Mining / 209
Dan L. Grecu and Lee A. Becker, Worcester Polytechnic Institute
Mining Segment-Wise
Periodic Patterns in Time-Related Databases / 214
Jiawei Han, Wan Gong and Yiwen Yin, Simon Fraser University
Learning to Predict
the Duration of an Automobile Trip / 219
Simon Handley and Pat Langley, Daimler-Benz Research and Technology Center; Folke A. Rauscher, Daimler-Benz AG
Fast Computation of
2-Dimensional Depth Contours / 224
Ted Johnson, AT&T Research Center; Ivy Kwok and Raymond Ng, University of British Columbia
Comparing Massive
High-Dimensional Data Sets / 229
Theodore Johnson and Tamraparni Dasu, AT&T Labs–Research
Defining the Goals
to Optimise Data Mining Performance / 234
Mark G. Kelly, David J. Hand and Niall M. Adams, The Open University
An Enhanced Representation
of Time Series Which Allows Fast and Accurate Classification,
Clustering and Relevance Feedback / 239
Eamonn J. Keogh and Michael J. Pazzani, University of California, Irvine
Active Templates:
Comprehensive Support for the Knowledge Discovery Process /
244
Randy Kerber, Hal Beck, Tej Anand and Bill Smart, NCR Human Interface Technology Center
Targeting Business
Users with Decision Table Classifiers / 249
Ron Kohavi and Daniel Sommerfield, Silicon Graphics, Inc.
BAYDA: Software
for Bayesian Classification and Feature Selection / 254
Petri Kontkanen, Petri Myllymäki, Tomi Silander and Henry Tirri, University of Helsinki
Approaches to Online
Learning and Concept Drift for User Identification in Computer Security
/ 259
Terran Lane and Carla E. Brodley, Purdue University
Human Performance
on Clustering Web Pages: A Preliminary Study / 264
Sofus A. Macskassy, Arunava Banerjee, Brian D. Davison and Haym Hirsh, Rutgers, The State University of New Jersey
Aggregation of
Imprecise and Uncertain Information for Knowledge Discovery in Databases
/ 269
Sally McClean, Bryan Scotney and Mary Shapcott, University of Ulster
Discovering Predictive
Association Rules / 274
Nimrod Megiddo and Ramakrishnan Srikant, IBM Almaden Research Center
Reinforcement Learning
for Trading Systems and Portfolios / 279
John Moody and Matthew Saffell, Oregon Graduate Institute
Group Bitmap Index:
A Structure for Association Rules Retrieval / 284
Tadeusz Morzy and Maciej Zakrzewicz, Poznan University of Technology
Towards the Personalization
of Algorithms Evaluation in Data Mining / 289
Gholamreza Nakhaeizadeh, Daimler-Benz AG and Alexander Schnabl, Technical University Vienna
Large Datasets Lead
to Overly Complex Models: An Explanation and a Solution / 294
Tim Oates and David Jensen, University of Massachusetts
Analysing Rock Samples
for the Mars Lander / 299
Jonathan Oliver, University of California, Berkeley; Ted Roush and Paul Gazis, NASA Ames Research Center;
Wray Buntine, Rohan Baxter and Steve Waterhouse, Ultimode Systems
Memory Placement
Techniques for Parallel Association Mining / 304
Srinivasan Parthasarathy, Mohammed J. Zaki and Wei Li, University of Rochester
Methods for Linking
and Mining Massive Heterogeneous Databases / 309
José C. Pinheiro and Don X. Sun, Bell Laboratories
Mining Databases
with Different Schemas: Integrating Incompatible Classifiers
/ 314
Andreas L. Prodromidis and Salvatore Stolfo, Columbia University
Time Series Forecasting
from High-Dimensional Data with Multiple Adaptive Layers / 319
R. Bharat Rao, Scott Rickard and Frans Coetzee, Siemens Corporate Research, Inc.
Ranking -- Methods
for Flexible Evaluation and Efficient Comparison of Classification
Performance / 324
Saharon Rosset, Tel Aviv University
A Robust System Architecture
for Mining Semi-Structured Data / 329
Lisa Singh, Bin Chen, Rebecca Haight, Peter Scheuermann and Kiyoko Aoki, Northwestern University
Defining diff
as a Data Mining Primitive / 334
Ramesh Subramonian, Intel Corporation
Simultaneous Reliability
Evaluation of Generality and Accuracy for Rule Discovery in Databases
/ 339
Einoshin Suzuki, Yokohama National University
Mining Generalized
Association Rules and Sequential Patterns Using SQL Queries
/ 344
Shiby Thomas, University of Florida and Sunita Sarawagi, IBM Almaden Research Center
Data Reduction Based
on Hyper Relations / 349
Hui Wang, Ivo Düntsch and David Bell, University of Ulster
Discovering Technical
Traders in the T-bond Futures Market/ 354
Andreas S. Weigend, Fei Chen and Stephen Figlewski, New York University; Steven R. Waterhouse, Ultimode Systems
Learning to Predict
Rare Events in Event Sequences / 359
Gary M. Weiss, AT&T Labs and Rutgers University, and Haym Hirsh, Rutgers University
Daily Prediction of
Major Stock Indices from Textual WWW Data / 364
B. Wüthrich, D. Permunetilleke, S. Leung, V. Cho, and J. Zhang, The Hong Kong University of Science and Technology, and W. Lam, The Chinese University of Hong Kong
PlanMine: Sequence
Mining for Plan Failures / 369
Mohammed J. Zaki, Neal Lesh and Mitsunori Ogihara, University of Rochester
Conference Report
Knowledge Discovery
and the Interface of Computing and Statistics / 375
Arnold Goodman, University of California, Irvine and John Elder IV, Elder Research
Index / 379
Tutorial
A Comparison of Leading Data Mining Tools
John F. Elder IV and Dean W. Abbott
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