Demystifying Information-Theoretic Clustering

Greg Ver Steeg; Aram Galstyan; Fei Sha; Simon DeDeo

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arXiv · 1310.4210

Demystifying Information-Theoretic Clustering

Greg Ver Steeg·Aram Galstyan·Fei Sha·Simon DeDeo

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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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cs.LG cs.IT math.IT physics.data-an stat.ML

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BibTeX RIS

Greg Ver Steeg, Aram Galstyan, Fei Sha, Simon DeDeo. 2014-02-05. Demystifying Information-Theoretic Clustering. https://arxiv.org/abs/1310.4210

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