Kernel-based modified fuzzy possibilistic c-means clustering

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Classification and clustering algorithms are, without doubt, a useful tool to explore data structures, and have been widely employed in many domains such as pattern recognition, image processing, data mining, and data analysis. The focus of this paper is the partitioning problem with a special interest in kernel method. The aim of this paper is to extend this method to the modified fuzzy possibilistic c-means (MFPCM) algorithm. It is realized by substitution of a kernelinduced distance metric for the Euclidean distance, and the corresponding algorithm is called kernel MFPCM algorithm. Numerical simulations are given to illustrate the performances of the proposed method.

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Fuzzy possibilistic c , means , modified fuzzy possibilistic c , means , kernel modified fuzzy possibilistic c , means , clustering

Mediterranean Telecommunications Journal

151

Mediterranean Telecommunications Journal