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A Method to Enhance the ‘Possibilistic C-Means with Repulsion’ Algorithm based on Cluster Validity Index

  • Juan Wachs
  • Oren Shapira
  • Helman Stern
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 34)

Abstract

In this paper, we examine the performance of fuzzy clustering algorithms as the major technique in pattern recognition. Both possibilistic and probabilistic approaches are explored. While the Possibilistic C-Means (PCM) has been shown to be advantageous over Fuzzy C-Means (FCM) in noisy environments, it has been reported that the PCM has an undesirable tendency to produce coincident clusters. Recently, an extension of the PCM has been presented by Timm et al., by introducing a repulsion term. This approach combines the partitioning property of the FCM with the robust noise insensibility of the PCM. We illustrate the advantages of both the possibilistic and probabilistic families of algorithms with several examples and discuss the PCM with cluster repulsion. We provide a cluster valid-ity function evaluation algorithm to solve the problem of parameter optimization. The algorithm is especially useful for the unsupervised case, when labeled data is unavailable.

Keywords

Fuzzy Cluster Membership Degree Cluster Validity Validity Function Possibilistic Approach 
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 2006

Authors and Affiliations

  • Juan Wachs
    • 1
  • Oren Shapira
    • 1
  • Helman Stern
    • 1
  1. 1.Department of Industrial Engineering and Management, Intelligent Systems DivisionBen-Gurion University of the NegevIsrael

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