Comparison of Cluster Validity Measures Basedx-Means

Abstract

Thex-means determines the suitable number of clusters automatically by executingk-means recursively. The Bayesian Information Criterion is applied to evaluate a cluster partition in thex-means. A novel type ofx-means clustering is proposed by introducing cluster validity measures that are used to evaluate the cluster partition and determine the number of clusters instead of the information criterion. The proposedx-means uses cluster validity measures in the evaluation step, and an estimation of the particular probabilistic model is therefore not required. The performances of a conventionalx-means and the proposed method are compared for crisp and fuzzy partitions using eight datasets. The comparison shows that the proposed method obtains better results than the conventional method, and that the cluster validity measures for a fuzzy partition are effective in the proposed method.