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Sparse Divisive Feature Clustering

Ndèye Niang-Keita 1 Mory Ouattara 1 Gilbert Saporta 1 
1 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : We propose an approach based on a divisive algorithm for clustering variables in order to identify in a large data table underlying dimensions that are not necessarily orthogonal. The number of clusters does not have to be defined in advance. The clusters, which are as unidimensional as possible, are then represented in a parsimonious way by a small number of variables or components.
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https://hal-cnam.archives-ouvertes.fr/hal-03475860
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Submitted on : Saturday, December 11, 2021 - 4:08:03 PM
Last modification on : Monday, February 21, 2022 - 3:38:18 PM
Long-term archiving on: : Saturday, March 12, 2022 - 6:22:12 PM

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Ndèye Niang-Keita, Mory Ouattara, Gilbert Saporta. Sparse Divisive Feature Clustering. XXVIII Meeting of the Portuguese Association for Classification and Data Analysis (JOCLAD 2021), CLAD, Dec 2021, Covilhã, Portugal. pp.75-76. ⟨hal-03475860⟩

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