Multidimensional Scaling and Data Clustering

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.

Authors

• Thomas Hofmann ETH Zürich
• Joachim Buhmann

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