Performance of eight cluster validity indices on hyperspectral data

This paper evaluates the performance of 5 previously presented in the literature cluster validity indices for the Fuzzy C-Means (FCM) clustering algorithm. The first two indices, the Fuzzy Partition Coefficient (PC), Fuzzy Partition Entropy Coefficient (PEC) select the number of clusters for which the fuzzy partition is more "crisp-like" or less fuzzy. The other three indices are the Fuzzy Davies-Bouldin Index (FDB), Xie-Beni Index (XB), and the Index I (I) choose the number of clusters which maximizes the inter-cluster separation and minimizes the within cluster scatter. A modification to these three indices is proposed based on the Bhattacharyya distance between clusters. The results show that this modification improves upon the performance of Index I. On the data sets presented on this paper the modifications of indices FDB and XB performed adequately.