Cluster Validity with Minimum Spanning Tree Based Clustering

Clustering is a process of discovering groups of objects such that the objects of the same group are similar, and objects belonging to different groups are dissimilar. A number of clustering algorithms exist that can solve the problem of clustering, but most of them are very sensitive to their input parameters. Therefore it is very important to evaluate the result of them. The minimum spanning tree clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose a minimum spanning tree based clustering algorithm with cluster evaluation. The algorithm produces k clusters with center and guaranteed intra-cluster similarity. The radius and diameter of the k clusters are computed to find the tightness of the k clusters. The variance of the k clusters is also computed to find the compactness of the clusters. In this paper we computed tightness and compactness of clusters, which reflects good measure of the efficacy of clustering.