Central and Pairwise Data Clustering by Competitive Neural Networks

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

Authors

Joachim Buhmann, Thomas Hofmann

Abstract

Data clustering amounts to a combinatorial optimization problem to re(cid:173) duce the complexity of a data representation and to increase its precision. Central and pairwise data clustering are studied in the maximum en(cid:173) tropy framework. For central clustering we derive a set of reestimation equations and a minimization procedure which yields an optimal num(cid:173) ber of clusters, their centers and their cluster probabilities. A meanfield approximation for pairwise clustering is used to estimate assignment probabilities. A se1fconsistent solution to multidimensional scaling and pairwise clustering is derived which yields an optimal embedding and clustering of data points in a d-dimensional Euclidian space.