A new clustering algorithm based on cluster validity indices

This paper addresses two most important issues in cluster analysis. The first issue pertains to the problem of deciding if two objects can be included in the same cluster. We propose a new similarity decision methodology which involves the idea of cluster validity index. The proposed methodology replaces a qualitative cluster recognition process with a quantitative comparison-based decision process. It obviates the need for complex parameters, a primary requirement in most clustering algorithms. It plays a key role in our new validation-based clustering algorithm, which includes a random clustering part and a complete clustering part. The second issue refers to the problem of determining the optimal number of clusters. The algorithm addresses this question through complete clustering which also utilizes the proposed similarity decision methodology. Experimental results are also provided to demonstrate the effectiveness and efficiency of the proposed algorithm.