A New Cluster Validity Index for Fuzzy Clustering

In this paper, we propose a new validity index for determining the number of clusters. It is based on a novel way of combining cohesion and discrepancy. Extensive tests of the index in a conventional model selection process (FCM algorithm) have been performed using generated data sets and public domain data sets,and comparison with several existing and important indices has been made. The results obtained show clearly the efficiency of the new index under the condition of overlapping clusters.