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blockcluster: An R Package for Model Based Co-Clustering

Parmeet Bhatia 1 Serge Iovleff 2, 1 Gérard Govaert 3 
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : Simultaneous clustering of rows and columns, usually designated by bi-clustering, co-clustering or block clustering, is an important technique in two way data analysis. A new standard and efficient approach have been recently proposed based on latent block model [Govaert and Nadif (2003)] which takes into account the block clustering problem on both the individual and variables sets. This article presents our R package for co-clustering of binary, contingency and continuous data blockcluster based on these very models. In this document, we will give a brief review of the model-based block clustering methods, and we will show how the R package blockcluster can be used for co-clustering.
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Parmeet Bhatia, Serge Iovleff, Gérard Govaert. blockcluster: An R Package for Model Based Co-Clustering. Journal of Statistical Software, University of California, Los Angeles, 2017, 76 (9), pp.24. ⟨10.18637/jss.v076.i09⟩. ⟨hal-01093554⟩

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