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Clustering analysis has been used for identifying similar objects and discovering distribution of patterns in large data sets. While hard clustering assigns an object to only one cluster, fuzzy clustering assigns one object to multiple clusters at the same time based on their degrees of membership. An important issue in clustering analysis is the validation of fuzzy partitions. In this paper, we consider the Bayesian like validation along with four conventional validity measures for two clustering algorithms namely, fuzzy c-means and fuzzy c-shell based. An empirical study is conducted on five data sets to compare their performances. Results show that the Bayesian validation score outperforms the conventional ones. However, a multiple objective approach is needed.

CONVENTIONAL AND BAYESIAN VALIDATION FOR FUZZY CLUSTERING ANALYSIS