Abstract
In the proposed work, our research focus is on semi-supervised clustering, which uses a small amount of supervised data in the form of class labels or pairwise constraints on some examples to aid unsupervised clustering. In addition, we have also included incomplete data with missing features. In the proposed clustering algorithm, we have embedded the optimal completion strategy (OCS) in semi-supervised clustering algorithm which imputes the missing values by the maximum likelihood estimate in an iterative optimization procedure. A series of detailed experimental analysis on real data sets is done to illustrate the main ideas discussed in the study work.
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