Study of semi-supervised clustering and challenge to constrained mixture distributions

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Existing methods of semi-supervised clustering and proposed methods by the authors are compared using artificial and repository data. Main results are as follows. 1. Agglomerative clustering performs as well as mixture distribution models in constrained clustering. 2.Methods of fuzzy clustering generalize basic mixture distribution models for semi-supervised classification. 3. Extensions of COP K-means have been proposed but they did not perform as well as constrained mixture distribution method. 4. The concept of inductive clustering has been proposed and its methodological usefulness has been shown. 5. Real data using Twitter have been analyzed using semi-supervised clustering and its effects have been investigated.