Principles of Data Mining and Knowledge Discovery

Third European Conference, PKDD’99, Prague, Czech Republic, September 15-18, 1999. Proceedings

Editors:

ISBN: 978-3-540-66490-1 (Print) 978-3-540-48247-5 (Online)

Table of contents (82 chapters)

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  1. Front Matter

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  2. Session 1A - Time Series

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      Pages 1-11

      Scaling up Dynamic Time Warping to Massive Datasets

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      Pages 12-22

      The Haar Wavelet Transform in the Time Series Similarity Paradigm

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      Pages 23-31

      Rule Discovery in Large Time-Series Medical Databases

  3. Session 1B - Applications

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      Pages 32-40

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

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      Pages 41-50

      Applying Data Mining Techniques to Wafer Manufacturing

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      Pages 51-60

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

  4. Session 2A - Taxonomies and Partitions

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      Pages 61-70

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

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      Pages 71-79

      Taxonomy Formation by Approximate Equivalence Relations, Revisited

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      Pages 80-88

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

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      Pages 89-97

      Speeding Up the Search for Optimal Partitions

  5. Session 2B - Logic Methods

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      Pages 98-106

      Experiments in Meta-level Learning with ILP

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      Pages 107-115

      Boolean Reasoning Scheme with Some Applications in Data Mining

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      Pages 116-124

      On the Correspondence between Classes of Implicational and Equivalence Quantifiers

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      Pages 125-135

      Querying Inductive Databases via Logic-Based User-Defined Aggregates

  6. Session 3A - Distributed and Multirelational Databases

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      Pages 136-146

      Peculiarity Oriented Multi-database Mining

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      Pages 147-155

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

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      Pages 156-164

      Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates

  7. Session 3B - Text Mining and Feature Selection

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      Pages 165-173

      Text Mining via Information Extraction

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      Pages 174-183

      TopCat: Data Mining for Topic Identification in a Text Corpus

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      Pages 184-192

      Selection and Statistical Validation of Features and Prototypes

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