Skip to main content
Springer Nature Link
Log in
Image

Principles of Data Mining and Knowledge Discovery

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

  • Conference proceedings
  • © 1999
Save conference proceedings
View saved research

Overview

Editors:
  1. Jan M. Żytkow

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

  2. Jan Rauch

    1. Faculty of Informatics and Statistics, University of Economics, Prague, Prague, Czech Republic

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1704)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: PKDD 1999.

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - view details

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

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.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects.

Table of contents (82 papers)

  1. Front Matter

    Download chapter PDF

  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
Back to top

Other volumes

  1. Principles of Data Mining and Knowledge Discovery

Editors and affiliations

  • 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

Accessibility information

Accessibility information for this book is coming soon. We're working to make it available as quickly as possible. Thank you for your patience.

Bibliographic information

Keywords

Publish with us

Policies and ethics

Back to top