Abstract
Much research has shown that fuzzy c-means clustering is a powerful tool for partitioning samples into different categories. However, the cost function of the classical fuzzy c-means (FCM) is defined by the distances from data to the cluster centers with their fuzzy memberships. In this study, a new fuzzy clustering algorithm, namely the fuzzy weighted c-means (FWCM), is proposed. In this proposed FWCM, the concept of weighted means using nonparametric weighted feature extraction (NWFE) is employed for replacing the cluster centers in the FCM. The experiments on both synthetic and real data show that the proposed clustering algorithm can generate better clustering results than FCM and the fuzzy compactness and separation (FCS) algorithms.
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