Validation of Fuzzy Partitions Obtained through Fuzzy C-Means Clustering

Abstract

A new cluster validity index is proposed to determine the optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index on well-known data sets showed its superior effectiveness and reliability in comparison to other indexes.