Sparse Weighted K-Means for Groups of Mixed-Type Variables
COTTRELL, Marie
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
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Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
COTTRELL, Marie
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
< Reduce
Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
Language
en
Ouvrage
This item was published in
2022, vol. 533
Springer International Publishing
English Abstract
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 ...Read more >
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.Read less <
Origin
Hal imported