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Demystifying Information-Theoretic Clustering
Authors: Greg V. Steeg, Aram Galstyan, Fei Sha and Simon Dedeo
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: We propose a novel method for clustering data which is grounded in information-theoretic principles and requires no parametric assumptions. Previous attempts to use information theory to define clusters in an assumption-free way are based on maximizing mutual information between data and cluster labels. We demonstrate that this intuition suffers from a fundamental conceptual flaw that causes clustering performance to deteriorate as the amount of data increases. Instead, we return to the axiomatic foundations of information theory to define a meaningful clustering measure based on the notion of consistency under coarse-graining for finite data.
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