A new cluster validity index for type-2 fuzzy c-means algorithm

Considering the growing application areas of type-2 fuzzy logic, this paper investigates the existing cluster validity indices that are suitable for FCM clustering algorithm. Based on the cluster fuzzy degree of FCM fuzzy set and the existing cluster validity indices, a new cluster validity index for the type-2 FCM called SM-index is proposed. The optimal partition or an optimal number of cluster, is obtained by maximizing the value of SM-index. The experimental results on the UCI and microarray data sets are reported to demonstrate the effectiveness of the proposed cluster validity index in appropriately determining the number of clusters.

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