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<p><b>Abstract</b>—Problems in data analysis often require the unsupervised partitioning of a data set into classes. Several methods exist for such partitioning but many have the weakness of being formulated via strict parametric models (e.g., each class is modeled by a single Gaussian) or being computationally intensive in high-dimensional data spaces. We reconsider the notion of such cluster analysis in information-theoretic terms and show that an efficient partitioning may be given via a minimization of partition entropy. A reversible-jump sampling is introduced to explore the variable-dimension space of partition models.</p>
Unsupervised data analysis, mixture models, Bayesian analysis, reversible-jump Markov Chain Monte Carlo, number of clusters.
Stephen J. Roberts, Chris Holmes, Dave Denison, "Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 23, no. , pp. 909-914, August 2001, doi:10.1109/34.946994
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