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We consider a semi-supervised clustering problem, where selected pairs of data points are labeled by an expert as must-links or cannot-links. Basically, must-link constraints indicate that two points should be grouped together, while those with cannot-link constraints should be grouped separately. We present a clustering algorithm, which creates a partition consistent with pairwise constraints by maximizing the probability of correct assignments. Moreover, unlabeled data are used by maximizingfatcat:sh7f7yvjn5gaxazp2gdny433vq