Determining the number of clusters in cluster analysis

DOI10.1016/J.JKSS.2007.10.004zbMath1298.62096OpenAlexW2090505730MaRDI QIDQ640693

My-Young Cheong, Hakbae Lee

Publication date: 19 October 2011

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jkss.2007.10.004

zbMATH Keywords

EM algorithmBayesian information criterioncluster analysisGibbs samplermixture modelmaximum a posterioriLaplace metropolis criteriamodified Fisher's criteria

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Clustering in the social and behavioral sciences (91C20) Stochastic particle methods (65C35)

Uses Software

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• Gibbsit

Cites Work

• Estimating the dimension of a model
• MCLUST: Software for model-based cluster analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Estimating Bayes Factors via Posterior Simulation With the Laplace-Metropolis Estimator
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Finite mixture models
• Bayes Factors
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