On a Hybrid Fuzzy Clustering Method

This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c–means (FCM) method as well as the fuzzy c–median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the L p norm. The values that minimize the proposed cost function have been chosen as the cluster prototypes. The weighted myriad is a special case of the cluster prototype when the L p norm is the L 2 norm. The cluster prototypes are the weighted meridians for the L 1 norm. Artificial data set is used to demonstrate the performance of proposed method, and the obtained results are compared to the results from the FCM method.

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