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Design analysis and implementation of efficient parameter free algorithm for high quality homogeneous clusters in data mining applications


Prasad S.Halgaonkar, Vijay M.Wadhai, A.D.Potgantwar


Vol. 10  No. 2  pp. 246-253


A new algorithm for clustering high-dimensional categorical data is proposed and implemented by us. Our algorithm is parameter-free, fully-automatic and is based on a two-phase iterative procedure. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. Second phase attempts to optimize clusters. This algorithm is parametric to cluster quality in terms of homogeneity. We show how a suitable notion of cluster homogeneity can be defined in the context of high-dimensional categorical data, from which an effective instance of the proposed clustering scheme immediately follows. Our experiments carried out on real data shows that the devised algorithm achieves optimal results in terms of compactness and separation.


Clustering, high-dimensional categorical data, information search and retrieval