An Extension of Possibilistic Fuzzy C-means using Regularization

허경용; 남궁영환; 김성훈

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 : Computer Science

허경용 ¹ , 남궁영환 ² , 김성훈 ³

¹ University of Florida
² University of Southern California
³ 경북대학교

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.
KEYWORDS

Fuzzy Clustering , Possibilistic Fuzzy C-means , Noise Sensitivity , Regularization
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KCI Citation (3)

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