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Abstract : |
Partitioning a set of data points which are characterized by their mutual dissimilarities instead of an explicit coordinate representation is a difficult, NP-hard combinatorial optimization problem. We formulate this optimization problem of a pairwise clustering cost function in the maximum entropy framework using a variational principle to derive corresponding data partitionings in a d-dimensional Euclidian space. This approximation solves the embedding problem and the grouping of these data into clusters simultaneously and in a selfconsistent fashion., |
