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Communication Dans Un Congrès Année : 2020

Sparse k-means for mixed data via group-sparse clustering

Résumé

The present manuscript tackles the issue of variable selection for clustering, in high dimensional data described both by numerical and categorical features. First, we build upon the sparse k-means algorithm with lasso penalty, and introduce the group-L1 penalty-already known in regression-in the unsupervised context. Second, we preprocess mixed data and transform categorical features into groups of dummy variables with appropriate scaling, on which one may then apply the group-sparse clustering procedure. The proposed method performs simultaneously clustering and feature selection, and provides meaningful partitions and meaningful features, numerical and categorical, for describing them.
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hal-03130672 , version 1 (03-02-2021)

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  • HAL Id : hal-03130672 , version 1

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Marie Chavent, Jerome Lacaille, Alex Mourer, Madalina Olteanu. Sparse k-means for mixed data via group-sparse clustering. ESANN 2020 - 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2020, Bruges / Virtual, Belgium. ⟨hal-03130672⟩
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