document: A probabilistic framework for semi-supervised clustering Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints , i.e. , pairs of instances labeled as belonging to same or different clusters . In recent years , a number of algorithms have been proposed for enhancing clustering quality by employing such supervision . Such methods use the constraints to either modify the objective function , or to learn the distance measure . We propose a probabilistic model for semi-supervised clustering based on Hidden Markov Random Fields ( HMRFs ) that provides a principled framework for incorporating supervision into prototype-based clustering . The model generalizes a previous approach that combines constraints and Euclidean distance learning , and allows the use of a broad range of clustering distortion measures , including Bregman divergences ( e.g. , Euclidean distance and I-divergence ) and directional similarity measures ( e.g. , cosine similarity ) . We present an algorithm that performs partitional semi-supervised clustering of data by minimizing an objective function derived from the posterior energy of the HMRF model . Experimental results on several text data sets demonstrate the advantages of the proposed framework .
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