A New Cluster Validity Index for Fuzzy Clustering

Performance of any clustering algorithm depends critically on the number of clusters that are initialized. A practitioner might not know, a priori, the number of partitions into which his data should be divided; to address this issue many cluster validity indices have been proposed for finding the optimal number of partitions. In this paper, we propose a new “Graded Distance index” (GD_index) for computing optimal number of fuzzy clusters for a given data set. The efficiency of this index is compared with well-known existing indices and tested on several data sets. It is observed that the “GD_index” is able to correctly compute the optimal number of partitions in most of the data sets that are tested.