Pairwise clustering using a Monte Carlo Markov Chain

Borko D. Stošić

Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, issue 12, 2373-2382

Abstract: In this work an application of MCMC is proposed for unsupervised data classification, in conjunction with a novel pairwise objective function, which is shown to work well in situations where clusters to be identified have a strong overlap, and the centroid oriented methods (such as K-means) fail by construction. In particular, an exceptionally simple but difficult situation is addressed when cluster centroids coincide, and one can differentiate between the clusters only on the basis of their variance. Performance of the proposed approach is tested on synthetic and real datasets.

Keywords: Clustering; MCMC; Quenching (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:388:y:2009:i:12:p:2373-2382

DOI: 10.1016/j.physa.2009.02.025

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