Application du coclustering à l'analyse exploratoire d'une table de données

Abstract : The cross-classification method is an unsupervised analysis technique that extracts the existing underlying structure between individuals and the variables in a data table as homogeneous blocks. This technique is limited to variables of the same type, either numerical or categorical, and we propose to extend it by proposing a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by discretization in equal frequency in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a coclustering method between the individuals and the binary variables, leading to groups of individual and groups of variable parts. We apply this methodology on several data sets and compare with the results of a multiple correspondence analysis MCA applied to the same data.
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Aichetou Bouchareb, Marc Boullé, Fabrice Clérot, Fabrice Rossi. Application du coclustering à l'analyse exploratoire d'une table de données. Conférence Internationale Francophone sur l'Extraction et gestion des connaissances (EGC 2017), Jan 2017, Grenoble, France. pp.177-188. ⟨hal-01469509⟩

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