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

¹University of Florida
²University of Southern California
³경북대학교

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
REFERENCES (14)

[journal] L.A.Zadeh / 1965 / Fuzzy sets / Information and Control 8(3) : 338~353

[book] J. Bezdek / 1981 / Pattern Recognition with Fuzzy Objective Function Algorithms / Springer

[journal] R.Krishnapuram / 1993 / A possibilistic approach to clustering / IEEE Transactions on Fuzzy Systems 1(2) : 98~110

[journal] N.R.Pal / 2005 / A possibilistic fuzzy c-means clustering algorithm / IEEE Transactions on Fuzzy Systems 13(4) : 517~530

[journal] J.S.Zhang / 2004 / Improved possibilistic c-means clustering algorithms / IEEE Transactions on Fuzzy Systems 12(2) : 209~217

[confproc] N.R.Pal / 1997 / A mixed c-means clustering model / Proceedings of 6th IEEE International Conference on Fuzzy Systems, : 11~21

[journal] A.Tikhonov / 1963 / On solving incorrectly posed problems and method of regularization / Dokl.Acad.Nauk USSR 151 : 501~504

[confproc] R.Li / 1995 / A maximum entropy approach to fuzzy clustering / Proceedings of 4th IEEE International Conference on Fuzzy Systems, : 2227~2232

[confproc] S.Miyamoto / 1998 / Fuzzy clustering by quadratic regularization / Proceedings of 4th IEEE International Conference on Fuzzy Systems, : 1394~1399

[journal] D. Ozdemir / 2002 / A fuzzy algorithm for color quantization of images / Pattern Recognition 35(8) : 1785~1791

[journal] J.Yu / 2007 / A generalized fuzzy clustering regularization model with optimality tests and model complexity analysis / IEEE Transactions on Fuzzy Systems 15(5) : 904~915

[journal] P.C.Hansen / 1992 / Analysis of discrete ill-posed problems by means of the L-curve / SIAM Review 34(4) : 561~580

[journal] D.K.Stando / 2007 / Regularization parameter selection in discrete ill-posed problems-the use of the U-curve / International Journal of Applied Mathematics and Computer Science 17(2) : 157~164

[report] C. W. Hsu / 2003 / A practical guide to support vector classification / Department of Computer Science, National Taiwan University
KCI Citation (3)

KJC Korea
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