Principles of Data Mining and Knowledge Discovery

1. Front Matter

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2. Session 1A - Time Series

   1. Scaling up Dynamic Time Warping to Massive Datasets

      • Eamonn J. Keogh, Michael J. Pazzani

      Pages 1-11

   2. The Haar Wavelet Transform in the Time Series Similarity Paradigm

      • Zbigniew R. Struzik, Arno Siebes

      Pages 12-22

   3. Rule Discovery in Large Time-Series Medical Databases

      • Shusaku Tsumoto

      Pages 23-31

3. Session 1B - Applications

   1. Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE

      • Hendrik Blockeel, Sašo Džeroski, Jasna Grbović

      Pages 32-40

   2. Applying Data Mining Techniques to Wafer Manufacturing

      • Elisa Bertino, Barbara Catania, Eleonora Caglio

      Pages 41-50

   3. An Application of Data Mining to the Problem of the University Students’ Dropout Using Markov Chains

      • S. Massa, P. P. Puliafito

      Pages 51-60

4. Session 2A - Taxonomies and Partitions

   1. Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD

      • Gou Masuda, Rei Yano, Norihiro Sakamoto, Kazuo Ushijima

      Pages 61-70

   2. Taxonomy Formation by Approximate Equivalence Relations, Revisited

      • F. A. El-Mouadib, J. Koronacki, J. M. Żytkow

      Pages 71-79

   3. On the Use of Self-Organizing Maps for Clustering and Visualization

      • Arthur Flexer

      Pages 80-88

   4. Speeding Up the Search for Optimal Partitions

      • Tapio Elomaa, Juho Rousu

      Pages 89-97

5. Session 2B - Logic Methods

   1. Experiments in Meta-level Learning with ILP

      • Ljupčo Todorovski, Sašo Džeroski

      Pages 98-106

   2. Boolean Reasoning Scheme with Some Applications in Data Mining

      • Andrzej Skowron, Hung Son Nguyen

      Pages 107-115

   3. On the Correspondence between Classes of Implicational and Equivalence Quantifiers

      • Jiří Ivánek

      Pages 116-124

   4. Querying Inductive Databases via Logic-Based User-Defined Aggregates

      • Fosca Giannotti, Giuseppe Manco

      Pages 125-135

6. Session 3A - Distributed and Multirelational Databases

   1. Peculiarity Oriented Multi-database Mining

      • Ning Zhong, Y. Y. Yao, Setsuo Ohsuga

      Pages 136-146

   2. Knowledge Discovery in Medical Multi-databases: A Rough Set Approach

      • Shusaku Tsumoto

      Pages 147-155

   3. Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates

      • Rónán Páircéir, Sally McClean, Bryan Scotney

      Pages 156-164

7. Session 3B - Text Mining and Feature Selection

   1. Text Mining via Information Extraction

      • Ronen Feldman, Yonatan Aumann, Moshe Fresko, Orly Liphstat, Binyamin Rosenfeld, Yonatan Schler

      Pages 165-173

   2. TopCat: Data Mining for Topic Identification in a Text Corpus

      • Chris Clifton, Robert Cooley

      Pages 174-183

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999.
The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.

• Data Mining
• Information Extraction
• Intelligent Data Analysis
• Text Mining
• database
• knowledge
• knowledge discovery
• logic

• Computer Science Department, UNC Charlotte, Charlotte, N.C. 28223 and Institute of Computer Science, Polish Academy of Sciences,  

  Jan M. Żytkow

• Faculty of Informatics and Statistics, University of Economics, Prague, Prague, Czech Republic

  Jan Rauch

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