Novel Validity Index for Fuzzy Clustering

A novel validity indice is proposed to determine the optimal number of clusters for fuzzy clustering. The novel validity indice considers the degree of compactness, the degree of overlapping, and the degree of separation. The compactness measures the similarity within a cluster. The degree of overlapping measures the overlap between clusters. Meanwhile, the degree of separation is used to measure the degree of the clear between clusters. The optimal cluster number can be effectively found by the proposed index.The experimental results show that the optimal cluster number are obtained which are general used in FCM algorithm.the new index overcomes the shortcomings of FCM that the cluster number must be pre-assigned and works well in the situations when there are overlapping subcluster in the clusters.