Generalized fuzzy clustering method

This paper presents a new hybrid fuzzy clustering m ethod. In the proposed method, cluster prototypes a r v lues that minimize the introduced generalized cost funct io . The proposed method can be considered as a gen eralization of fuzzy c-means (FCM) method as well as the fuzzy c-m edian (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The values that minimize the proposed cost f unction have been chosen as the group prototypes. The weighted myriad is the special case of the group prototype, when t Lp norm is the L2 (Euclidean) norm. The cluster prototypes are the w eighted meridians for the L1 norm. Artificial data set is used to demonstrate the performance of proposed method.

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