Abstract
Partitioning a data set and extracting hidden structure from the data arises in different application areas of pattern recognition, speech and image processing. Pairwise data clustering is a combinatorial optimization method for data grouping which extracts hidden structure from proximity data. We describe a deterministic annealing approach to pairwise clustering which shares the robustness properties of maximum entropy inference. The resulting Gibbs probability distributions are estimated by mean-field approximation. A new structure-preserving algorithm to cluster dissimilarity data and to simultaneously embed these data in a Euclidian vector space is discussed which can be used for dimensionality reduction and data visualization. The suggested embedding algorithm which outperforms conventional approaches has been implemented to analyze dissimilarity data from protein analysis and from linguistics. The algorithm for pairwise data clustering is used to segment textured images.
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