Nonparametric Bayesian Clustering Ensembles

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Nonparametric Bayesian Clustering Ensembles
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.
Pu Wang, Carlotta Domeniconi, Kathryn Blackmond La
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where PKDD
Authors Pu Wang, Carlotta Domeniconi, Kathryn Blackmond Laskey
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