HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download

Contributor : Fabrice Rossi Connect in order to contact the contributor
Submitted on : Thursday, February 16, 2017 - 3:09:22 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
Long-term archiving on: : Wednesday, May 17, 2017 - 7:43:29 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License


  • HAL Id : hal-01469509, version 1


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⟩



Record views


Files downloads