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Possibilistic Approach to Kernel-Based Fuzzy c-Means Clustering with Entropy Regularization

  • Kiyotaka Mizutani
  • Sadaaki Miyamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3558)

Abstract

The fuzzy c-means (FCM) is sensitive to noise or outliers because this method has the probabilistic constraint that the memberships of a data point across classes sum to one. To solve the problem, a possibilistic c-means clustering (PCM) has been proposed by Krishnapuram and Keller. An advantage of PCM is highly robust in a noisy environment. On the other hand, some clustering algorithms using the kernel trick, e.g., kernel-based FCM and kernel-based LVQ clustering, have been studied to obtain nonlinear classification boundaries. In this paper, an entropy-based possibilistic c-means clustering using the kernel trick has been proposed as more robust method. Numerical examples are shown and effect of the kernel method is discussed.

Keywords

Probabilistic Constraint Kernel Principal Component Analysis Kernel Trick Fisher Discriminant Analysis 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-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kiyotaka Mizutani
    • 1
  • Sadaaki Miyamoto
    • 2
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan
  2. 2.Department of Risk Engineering, School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan

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