Abstract
Fuzzy clustering is used to cluster data such that each point may pertain to a number of clusters. Fuzzy clustering has applications in the areas like pattern recognition, medical segmentation, recommender systems etc. Kernel based clustering algorithms are performing better as in Kernel based algorithms, clustering is performed by mapping data to high dimension space. RBF kernel type-2 fuzzy-c-means (RKT2FCM) algorithm has been proposed using the RBF function in this paper. A few standard datasets are used to better analysis and compare performance of proposed algorithm with Fuzzy-C-Means (FCM), Type-2 Fuzzy-C-Means (T2FCM), Kernalized Fuzzy-C-Means (KFCM), FCM with new distance metric (FCM-σ). KT2FCM algorithm uses RBF mapping function and hence gives better performance. Experimental analysis of the proposed algorithm has been given in this paper.
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