1. Clustering and Fuzzy Clustering

  1. Witold Pedrycz1,2

Published Online: 27 JAN 2005

DOI: 10.1002/0471708607.ch1

Knowledge-Based Clustering: From Data to Information Granules

Knowledge-Based Clustering: From Data to Information Granules

How to Cite

Pedrycz, W. (2005) Clustering and Fuzzy Clustering, in Knowledge-Based Clustering: From Data to Information Granules, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471708607.ch1

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:

  • hierarchical and objective function based clustering;
  • FCM;
  • cluster validity and cluster geometry;
  • possibilistic clustering;
  • self-organizing maps

Summary

The chapter is a comprehensive introduction to the area of clustering and fuzzy clustering by providing the reader with the basic notions and notation, main categories of clustering techniques and motivation behind the introduction of fuzzy clusters and partial membership to clusters. The reference model of clustering is the well-known objective function based Fuzzy C-Means (FCM). The geometry of the FCM and its variants is discussed along with its main extensions such as possibilistic clustering and noise clustering. Contrasted are self-organizing maps and the FCM algorithm.