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Conference papers

Co-clustering de données mixtes à base des modèles de mélange

Abstract : Co-clustering is a data mining technique used to extract the underlying block structure between the rows and columns of a data matrix. Many approaches have been studied and have shown their capacity to extract such structures in continuous, binary or contingency tables. However, very little work has been done to perform co-clustering on mixed type data. In this article, we extend the use of latent bloc models to co-clustering in the case of mixed data (continuous and binary variables). We then evaluate the effectiveness of our extension on simulated data and we discuss its potential limits.
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Submitted on : Thursday, February 16, 2017 - 3:20:33 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
Long-term archiving on: : Wednesday, May 17, 2017 - 7:52:18 PM


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  • HAL Id : hal-01469546, version 1


Aichetou Bouchareb, Marc Boullé, Fabrice Rossi. Co-clustering de données mixtes à base des modèles de mélange. Conférence Internationale Francophone sur l'Extraction et gestion des connaissances (EGC 2017), Jan 2017, Grenoble, France. pp.141-152. ⟨hal-01469546⟩



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