Nonparametric Bayesian clustering ensembles

Authors:
Pu Wang;Carlotta Domeniconi;Kathryn Blackmond Laskey
Affiliations:
Department of Computer Science, George Mason University, Fairfax, VA;Department of Computer Science, George Mason University, Fairfax, VA;Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Year:
2010

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Abstract

Forming consensus clusters from multiple input clusterings can improve accuracy and robustness. Current clustering ensemble methods require specifying the number of consensus clusters. A poor choice can lead to under or over fitting. This paper proposes a nonparametric Bayesian clustering ensemble (NBCE) method, which can discover the number of clusters in the consensus clustering. Three inference methods are considered: collapsed Gibbs sampling, variational Bayesian inference, and collapsed variational Bayesian inference. Comparison of NBCE with several other algorithms demonstrates its versatility and superior stability.