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Utilisation des modèles de co-clustering pour l'analyse exploratoire des données

Abstract : Co-clustering is a clustering technique aiming at simultaneously partitioning the rows and the columns of a data matrix. Among the existing approaches, MODL is suitable for processing huge data sets with several continuous or categorical variables. We use it as the baseline approach in this thesis. We discuss the reliability of applying such an approach on data mining problems like graphs partitioning, temporal graphs segmentation or curve clustering. MODL tracks very fine patterns in huge data sets, that makes the results difficult to study. That is why, exploratory analysis tools must be defined in order to explore them. In order to help the user in interpreting the results, we define exploratory analysis tools aiming at simplifying the results in order to make possible an overall interpretation, tracking the most interesting patterns, determining the most representative values of the clusters and visualizing the results. We investigate the asymptotic behavior of these exploratory analysis tools in order to make the connection with the existing approaches. Finally, we highlight the value of MODL and the exploratory analysis tools owing to an application on call detailed records from the telecom operator Orange, collected in Ivory Coast.
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Submitted on : Thursday, January 23, 2014 - 12:20:23 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
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  • HAL Id : tel-00935278, version 1


Romain Guigourès. Utilisation des modèles de co-clustering pour l'analyse exploratoire des données. Applications [stat.AP]. Université Panthéon-Sorbonne - Paris I, 2013. Français. ⟨tel-00935278⟩



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