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We investigate the following problem: Given a set of candidate clusterings for a common set of objects, find a centroid clustering that is most compatible to the input set. First, we propose a series of entropy-based distance functions for comparing various clusterings. Such functions enable us to directly select the local centroid from the candidate set. Second, we present two combining methods for...
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