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An Extension of Possibilistic Fuzzy C-means using Regularization

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2010, 15(1), pp.43-50
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

허경용 1 남궁영환 2 김성훈 3

1University of Florida
2University of Southern California
3경북대학교

Accredited

ABSTRACT

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are the two most well-known clustering algorithms in fuzzy clustering area, and have been applied in many applications in their original or modified forms. However, FCM's noise sensitivity problem and PCM's overlapping cluster problem are also well known. Recently there have been several attempts to combine both of them to mitigate the problems and possibilistic fuzzy c-means (PFCM) showed promising results. In this paper, we proposed a modified PFCM using regularization to reduce noise sensitivity in PFCM further. Regularization is a well-known technique to make a solution space smooth and an algorithm noise insensitive. The proposed algorithm, PFCM with regularization (PFCM-R), can take advantage of regularization and further reduce the effect of noise. Experimental results are given and show that the proposed method is better than the existing methods in noisy conditions.

Citation status

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