Model selection for Gaussian latent block clustering with the integrated classification likelihood

DOI10.1007/s11634-013-0161-3zbMath1416.62349OpenAlexW2057245283MaRDI QIDQ75065

Gérard Govaert, Aurore Lomet, Gérard Govaert, Aurore Lomet, Yves Grandvalet

Publication date: 9 February 2014

Published in: Advances in Data Analysis and Classification, Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-013-0161-3

zbMATH Keywords

model selectionBICcontinuous dataco-clusteringlatent block modelintegrated classification likelihood

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)

Related Items (7)

Goodness-of-fit test for latent block models ⋮ Unifying data units and models in (co-)clustering ⋮ A goodness-of-fit test on the number of biclusters in a relational data matrix ⋮ A survey on model-based co-clustering: high dimension and estimation challenges ⋮ Selective inference for latent block models ⋮ blockcluster ⋮ Learning from missing data with the binary latent block model

Uses Software

• BayesDA

Cites Work

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• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• An improvement of the NEC criterion for assessing the number of clusters in a mixture model
• Estimation and selection for the latent block model on categorical data

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