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Fuzzy Cluster Analysis from the Viewpoint of Robust Statistics

  • Frank Klawonn
  • Frank Höppner
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 243)

Introduction

Fuzzy cluster analysis has been initiated in the beginning of the seventies by Bezdek [1], [3] and Dunn [12]. The ideas were partly motivated by the problems caused by the binary or crisp assignment of data to unique clusters as for instance in the case of the popular c-means clustering algorithm. Handling ambiguous and noisy data in order to overcome these problems was one important issue.

Although such concepts of robustness were part of the motivation for introducing fuzzy clustering, serious attempts to a rigorous analysis of robustness issues in fuzzy clustering have not been made until the mid-nineties.

Keywords

Fuzzy Cluster Membership Degree Robust Regression Robust Statistic Breakdown Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Frank Klawonn
    • 1
  • Frank Höppner
    • 1
  1. 1.No Affiliations 

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