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Fuzzy Shell Cluster Analysis

  • F. Klawonn
  • R. Kruse
  • H. Timm
Part of the International Centre for Mechanical Sciences book series (CISM, volume 382)

Abstract

In this paper we survey the main approaches to fuzzy shell cluster analysis which is simply a generalization of fuzzy cluster analysis to shell like clusters, i.e. clusters that lie in nonlinear subspaces. Therefore we introduce the main principles of fuzzy cluster analysis first. In the following we present some fuzzy shell clustering algorithms In many applications it is necessary to determine the number of clusters as well as the classification of the data set. Subsequently therefore we review the main ideas of unsupervised fuzzy shell cluster analysis. Finally we present an application of unsupervised fuzzy shell cluster analysis in computer vision.

Keywords

Cluster Algorithm Fuzzy Cluster Membership Degree Fuzzy Cluster Algorithm Linear Cluster 
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 Wien 1997

Authors and Affiliations

  • F. Klawonn
    • 1
  • R. Kruse
    • 1
  • H. Timm
    • 1
  1. 1.University of MagdeburgMagdeburgGermany

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