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Article Dans Une Revue Advances in Data Analysis and Classification Année : 2018

Model Selection for Gaussian Latent Block Clustering with the Integrated Classification Likelihood

Résumé

For a given data table, several candidate models are usually examined, which differ for example in the number of clusters. Model selection then becomes a critical issue. To this end, we develop a criterion based on an approximation of the Integrated Classification Likelihood for the Gaussian latent block model, and propose a BIC-like variant \yg{following the same pattern}. We also propose a non-asymptotic exact criterion, thus circumventing the controversial definition of the asymptotic regime arising from the dual nature of the rows and columns \yg{in co-clustering}. The experimental results show steady performances of these criteria for medium to large data tables.
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Dates et versions

hal-00913680 , version 1 (04-12-2013)

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Aurore Lomet, Gérard Govaert, Yves Grandvalet. Model Selection for Gaussian Latent Block Clustering with the Integrated Classification Likelihood. Advances in Data Analysis and Classification, 2018, 12 (3), pp.489-508. ⟨10.1007/s11634-013-0161-3⟩. ⟨hal-00913680⟩
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