A Probabilistic Cluster Validity Index for Agglomerative Bayesian Fuzzy Clustering
Sang Wan Lee, Yong Soo Kim, Zeungnam Bien · 2008
A novel fuzzy clustering technique, called Agglomerative Iterative Bayesian Fuzzy Clustering (IBFC) with a novel cluster validity index is presented. The algorithm has a fuzzy competitive learning structure properly incorporated with Bayesian decision rule. Based on this Bayesian assumption, we propose a probabilistic cluster validity index, by which an optimal number of clusters is determined. We reports that the proposed algorithm shows better performance when tested with synthetic/benchmark data and compared with several well-known methods.