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We propose an adaptive constrained clustering (ACC) algorithm that performs clustering and feature weighting simultaneously and that can incorporate partial supervision information. This information consists of a set of constraints on which instances should or should not reside in the same cluster. The algorithm is dynamic in the sense that the optimal number of clusters can expand or shrink depending...
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