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Constraint-free Optimal Dual Similarity Validity Clusters Using Dynamic Minimum Spanning Tree


S. John Peter, S.P. Victor


Vol. 10  No. 7  pp. 253-261


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 constraint-free minimum spanning tree based clustering algorithm. The algorithm constructs hierarchy from top to bottom. At each hierarchical level, it optimizes the number of cluster, from which the proper hierarchical structure of underlying dataset can be found. The algorithm uses a new cluster validation criterion based on the geometric property of data partition of the data set in order to find the proper number of clusters at each level. The radius and diameter of the clusters are computed to find the tightness of the individual clusters. The variance of the clusters is also computed to find the compactness of the individual clusters. In this paper we compute tightness and compactness of clusters, which reflects good measure of the efficacy of clustering. The algorithm works in two phases. The first phase of the algorithm produces subtrees. The second phase converts the subtrees into dendrogram. The key feature of the algorithm is it uses both divisive and agglomerative approaches to find optimal Dual similarity clusters.


Euclidean minimum spanning tree, Clustering, Eccentricity, Center, Hierarchical clustering, Dendrogram, Subtree, Cluster validity, Cluster Separation