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An Analysis of Model-based Clustering , Competitive Learning , and Information Bottleneck

This paper provides a general formulation of probabilistic model-based clustering with deterministic annealing (DA), which leads to a unifying analysis of k-means, EM clustering, soft competitive learning algorithms (e.g., self-organizing map), and information bottleneck. The analysis points out an interesting yet not well-recognized connection between the k-means and EM clustering—they are just two different stages of a DA clustering process, with different temperatures. Demonstrated relationships between modelbased clustering, competitive learning, and information bottleneck, can potentially generate a series of new algorithms.