Multidimensional Scaling and Data Clustering

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Thomas Hofmann, Joachim Buhmann


Visualizing and structuring pairwise dissimilarity data are difficult combinatorial op(cid:173) timization problems known as multidimensional scaling or pairwise data clustering. Algorithms for embedding dissimilarity data set in a Euclidian space, for clustering these data and for actively selecting data to support the clustering process are discussed in the maximum entropy framework. Active data selection provides a strategy to discover structure in a data set efficiently with partially unknown data.