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Title |
An Efficient Semi Supervised Clustering Techniques with Pairwise Constraints |
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Author |
M. Pavithra, Dr.R.M.S.Parvathi |
| Citation |
Vol. 25 No. 2 pp. 211-217 |
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Abstract |
Semi-supervised clustering leverages side information such as pairwise constraints to guide clustering procedures. Despite promising progress, existing semi-supervised clustering approaches overlook the condition of side information being generated sequentially, which a natural setting is arising in numerous real-world applications such as social network and e-commerce system analysis. We consider the semi-supervised clustering problem where we know (with varying degree of certainty) that some sample pairs are (or are not) in the same class. Unlike previous efforts in adapting clustering algorithms to incorporate those pairwise relations, our work is based on a discriminative model. According to the principle of ensemble clustering, the optimal partition lies in the convex hull, and can thus be uniquely represented by an m-dimensional probability simplex vector. As such, the dynamic semi-supervised clustering problem is simpli?ed to the problem of updating a probability simplex vector subject to the newly received pairwise constraints. We then develop a computationally ef?cient updating procedure to update the probability simplex vector in O (m2) time, irrespective of the data size n. Our empirical studies on several real-world benchmark datasets show that the proposed algorithm outperforms the state of-the-art semi-supervised clustering algorithms with visible performance gain and signi?cantly reduced running time. |
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Keywords |
Data Mining, Knowledge Discovery in Databases, Clustering, Semi Supervised Clustering, Pairwise Constraints. |
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