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Co-clustering based exploratory analysis of mixed-type data tables

hal.structure.identifierOrange Labs [Lannion]
hal.structure.identifierStatistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
dc.contributor.authorBouchareb, Aichetou
hal.structure.identifierOrange Labs [Lannion]
dc.contributor.authorBoullé, Marc
hal.structure.identifierOrange Labs [Lannion]
dc.contributor.authorClérot, Fabrice
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRossi, Fabrice
dc.date.accessioned2023-01-23T09:57:35Z
dc.date.available2023-01-23T09:57:35Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.psl.eu/handle/123456789/23800
dc.language.isoenen
dc.subject.ddc519en
dc.titleCo-clustering based exploratory analysis of mixed-type data tablesen
dc.typeChapitre d'ouvrage
dc.description.abstractenCo-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering method based on a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by equal frequency discretization in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a co-clustering to the instances and the binary variables, leading to groups of instances and groups of variable parts. We apply this methodology on several data sets and compare with the results of a Multiple Correspondence Analysis applied to the same data.en
dc.identifier.citationpages23-41en
dc.relation.ispartofseriestitleStudies in Computational Intelligence (SCI, volume 834)en
dc.relation.ispartoftitleAdvances in Knowledge Discovery and Management, Volume 8en
dc.relation.ispartofeditorBruno Pinaud, Fabrice Guillet, Fabien Gandon, Christine Largeron
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofdate2019
dc.relation.ispartofpages183en
dc.relation.ispartofurl10.1007/978-3-030-18129-1en
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.ispartofisbn978-3-030-18129-1en
dc.relation.forthcomingnonen
dc.identifier.doi10.1007/978-3-030-18129-1_2en
dc.description.ssrncandidatenon
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2023-01-23T09:51:56Z
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