Hybrid Algorithm for Noise-free High Density Clusters with Self-Detection of Best Number of Clusters

Clustering is a process of discovering group 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. Minimum Spanning Tree clustering algorithm is capable of detecting clusters with irregular boundaries. A density-based notion of clusters which is designed to discover clusters of arbitrary shape. In this paper we propose a combined approach based on Minimum Spanning Tree based clustering and Density-based clustering for noise-free high density best number of clusters. 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 algorithm works in two phases. The first phase of the algorithm produces subtrees (noise-free clusters). The second phase finds high density clusters from the subtrees.

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