CHAVENT, Marie; OLTEANU, Madalina; COTTRELL, Marie; LACAILLE, Jérôme; MOURER, Alex

doi:10.1007/978-3-031-15444-7

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CHAVENT, Marie
Méthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]

OLTEANU, Madalina
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]

COTTRELL, Marie
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]

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2022, vol. 533

Springer International Publishing

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Assessing 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.< Leer menos

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

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