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http://dx.doi.org/10.5391/JKIIS.2004.14.7.889

A Kernel based Possibilistic Approach for Clustering and Image Segmentation  

Choi, Kil-Soo (한양대학교 전자전기제어계측공학과)
Choi, Byung-In (한양대학교 전자전기제어계측공학과)
Rhee, Chung-Hoon (한양대학교 전자전기제어계측공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.7, 2004 , pp. 889-894 More about this Journal
Abstract
The fuzzy kernel c-means (FKCM) algorithm, which uses a kernel function, can obtain more desirable clustering results than fuzzy c-means (FCM) for not only spherical data but also non-spherical data. However, it can be sensitive to noise as in the FCM algorithm. In this paper, a kernel function is applied to the possibilistic c-means (PCM) algorithm and is shown to be robust for data with additive noise. Several experimental results show that the proposed kernel possibilistic c-means (KPCM) algorithm out performs the FKCM algorithm for general data with additive noise.
Keywords
Kernel; Clustering; FKCM; KPCM; Noise;
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