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Principles of Data Mining and Knowledge Discovery

4th European Conference, PKDD, 2000, Lyon, France, September 13-16, 2000 Proceedings

  • Conference proceedings
  • © 2000

Overview

Editors:
  1. Djamel A. Zighed

    1. Laboratoire ERIC, Université Lyon 2, Bron, France

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  2. Jan Komorowski

    1. Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

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    You can also search for this editor in PubMed   Google Scholar

  3. Jan Żytkow

    1. Department of Computer Science, University of North Carolina, Charlotte, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1910)

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

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Table of contents (86 papers)

  1. Front Matter

    Pages I-XV
    Download chapter PDF

  2. Towards Broader Foundations

    1. Multi-relational Data Mining, Using UML for ILP

      • Arno J. Knobbe, Arno Siebes, Hendrik Blockeel, Daniël Van Der Wallen
      Pages 1-12

    2. An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

      • Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
      Pages 13-23

    3. Basis of a Fuzzy Knowledge Discovery System

      • Maurice Bernadet
      Pages 24-33

  3. Rules and Trees

    1. Confirmation Rule Sets

      • Dragan Gamberger, Nada Lavrač
      Pages 34-43

    2. Contribution of Dataset Reduction Techniques to Tree-Simplification and Knowledge Discovery

      • Marc Sebban, Richard Nock
      Pages 44-53

    3. Combining Multiple Models with Meta Decision Trees

      • Ljupčo Todorovski, Sašo Džeroski
      Pages 54-64

  4. Databases and Reward-Based Learning

    1. Materialized Data Mining Views

      • Tadeusz Morzy, Marek Wojciechowski, Maciej Zakrzewicz
      Pages 65-74

    2. Approximation of Frequency Queries by Means of Free-Sets

      • Jean-François Boulicaut, Artur Bykowski, Christophe Rigotti
      Pages 75-85

    3. Application of Reinforcement Learning to Electrical Power System Closed-Loop Emergency Control

      • C. Druet, D. Ernst, L. Wehenkel
      Pages 86-95

    4. Efficient Score-Based Learning of Equivalence Classes of Bayesian Networks

      • Paul Munteanu, Denis Cau
      Pages 96-105

  5. Classication

    1. Quantifying the Resilience of Inductive Classification Algorithms

      • Melanie Hilario, Alexandros Kalousis
      Pages 106-115

    2. Bagging and Boosting with Dynamic Integration of Classifiers

      • Alexey Tsymbal, Seppo Puuronen
      Pages 116-125

    3. Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information

      • Carlos Soares, Pavel B. Brazdil
      Pages 126-135

    4. Some Enhancements of Decision Tree Bagging

      • Pierre Geurts
      Pages 136-147

  6. Association Rules and Exceptions

    1. Relative Unsupervised Discretization for Association Rule Mining

      • Marcus-Christopher Lud, Gerhard Widmer
      Pages 148-158

    2. Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today’s Approaches

      • Jochen Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh
      Pages 159-168

    3. Unified Algorithm for Undirected Discovery of Exception Rules

      • Einoshin Suzuki, Jan M. Żytkow
      Pages 169-180

    4. Sampling Strategies for Targeting Rare Groups from a Bank Customer Database

      • J-H. Chauchat, R. Rakotomalala, D. Robert
      Pages 181-190

  7. Instance-Based Discovery

    1. Instance-Based Classification by Emerging Patterns

      • Jinyan Li1, Guozhu Dong, Kotagiri Ramamohanarao
      Pages 191-200
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Keywords

Editors and Affiliations

  • Laboratoire ERIC, Université Lyon 2, Bron, France

    Djamel A. Zighed

  • Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway

    Jan Komorowski

  • Department of Computer Science, University of North Carolina, Charlotte, USA

    Jan Żytkow

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