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Computational Intelligence in Data Mining

  • Conference proceedings
  • © 2000

Overview

Editors:
  1. Giacomo Riccia

    1. University of Udine, Italy

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  2. Rudolf Kruse

    1. Otto-Von-Guericke University, Germany

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  3. Hanz-J. Lenz

    1. Free University of Berlin, Germany

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Part of the book series: CISM International Centre for Mechanical Sciences (CISM, volume 408)

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Table of contents (9 papers)

  1. Front Matter

    Pages ii-vii
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  2. Data Mining and Statistics

    • A. Siebes
    Pages 1-38

  3. Subgroup Mining

    • W. Klösgen
    Pages 39-49

  4. Possibilistic Graphical Models

    • C. Borgelt, J. Gebhardt, R. Kruse
    Pages 51-67

  5. An Overview of Possibilistic Logic and its Application to Nonmonotonic Reasoning and Data Fusion

    • S. Benferhat, D. Dubois, H. Prade
    Pages 69-93

  6. On the Solution of Fuzzy Equation Systems

    • H.-J. Lenz, R. Müller
    Pages 95-110

  7. Learning Fuzzy Models and Potential Outliers

    • M. R. Berthold
    Pages 111-126

  8. An Algorithm for Adaptive Clustering and Visualisation of Highdimensional Data Sets

    • F. Schwenker, H. A. Kestler, G. Palm
    Pages 127-140

  9. Learning in Computer Soccer

    • H.-D. Burkhard
    Pages 141-151

  10. Controlling Based on Stochastic Models

    • H.-J. Lenz, E. Rödel
    Pages 153-164

  11. Back Matter

    Pages 165-166
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Keywords

About this book

The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on ‘Data Mining and Statistics – A System Point of View’. Two special techniques follow: ‘Subgroup Mining’, and ‘Data Mining with Possibilistic Graphical Models’. "Data Fusion and Possibilistic or Fuzzy Data Analysis” is the next area of interest. An overview of possibilistic logic, nonmonotonic reasoning and data fusion is given, the coherence problem between data and non-linear fuzzy models is tackled, and outlier detection based on learning of fuzzy models is studied. In the domain of "Classification and Decomposition” adaptive clustering and visualisation of high dimensional data sets is introduced. Finally, in the section "Learning and Data Fusion” learning of special multi-agents of virtual soccer is considered. The last topic is on data fusion based on stochastic models.

Editors and Affiliations

  • University of Udine, Italy

    Giacomo Riccia

  • Otto-Von-Guericke University, Germany

    Rudolf Kruse

  • Free University of Berlin, Germany

    Hanz-J. Lenz

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