Kernel-based fuzzy and possibilistic c-means clustering
نویسندگان
چکیده
The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algorithms are called kernel fuzzy c-means (KFCM) and kernel possibilistic c-means (KPCM) algorithms. And some test results are given to illustrate the advantages of the proposed algorithms over the FCM and PCM algorithms.
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