10. Fuzzy Clustering of Heterogeneous Patterns

  1. Witold Pedrycz1,2

Published Online: 27 JAN 2005

DOI: 10.1002/0471708607.ch10

Knowledge-Based Clustering: From Data to Information Granules

Knowledge-Based Clustering: From Data to Information Granules

How to Cite

Pedrycz, W. (2005) Fuzzy Clustering of Heterogeneous Patterns, in Knowledge-Based Clustering: From Data to Information Granules, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471708607.ch10

Author Information

  1. 1

    Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

  2. 2

    Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Publication History

  1. Published Online: 27 JAN 2005
  2. Published Print: 7 JAN 2005

ISBN Information

Print ISBN: 9780471469667

Online ISBN: 9780471708605

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Keywords:

  • heterogeneous data;
  • parametric and nonparametric models of data;
  • possibility-necessity transformation;
  • encoding;
  • decoding

Summary

Discussed is clustering of heterogeneous data that are patterns whose features are information granules rather than numeric entities. Those are represented with the aid of fuzzy sets and fuzzy relations. The fundamental design phase concerns the representation of information granules for clustering and representation of results (e.g., prototypes) that leads to several mechanisms of encoding and decoding. Two fundamental approaches to clustering known as parametric and nonparametric techniques are analyzed.