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Sparse Weighted K-Means for Groups of Mixed-Type Variables

hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorOLTEANU, Madalina
hal.structure.identifierStatistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
dc.contributor.authorCOTTRELL, Marie
hal.structure.identifierSafran Aircraft Engines
dc.contributor.authorLACAILLE, Jérôme
hal.structure.identifierStatistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
dc.contributor.authorMOURER, Alex
dc.date.accessioned2024-04-04T02:39:58Z
dc.date.available2024-04-04T02:39:58Z
dc.date.issued2022
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191026
dc.description.abstractEnAssessing the underlying structure of a dataset is often done by training a clustering procedure on the features describing the data. Inpractice, while the data may be described by a large number of features, only a minority of them may be actually informative with regard to thestructure. Furthermore, redundant features may also bias the clustering, whether one speaks of redundancy in the informative or the uninformativefeatures. The present contribution aims at illustrating two sparse clustering methods designed for mixed data (made of numerical and categoricalfeatures). The proposed methods summarise redundant features into groups, and select the most relevant groups of features only in theclustering procedure. The performances and the interpretability of the sparse methods are illustrated on a real-life data set.
dc.language.isoen
dc.publisherSpringer International Publishing
dc.publisher.locationCham
dc.subject.meshSparse clustering, feature clustering, feature selection, group of features selection, variable importance.
dc.title.enSparse Weighted K-Means for Groups of Mixed-Type Variables
dc.typeOuvrage
dc.identifier.doi10.1007/978-3-031-15444-7
dc.subject.halStatistiques [stat]/Calcul [stat.CO]
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
bordeaux.volume533
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-03827823
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03827823v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2022&rft.volume=533&rft.au=CHAVENT,%20Marie&OLTEANU,%20Madalina&COTTRELL,%20Marie&LACAILLE,%20J%C3%A9r%C3%B4me&MOURER,%20Alex&rft.genre=unknown


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