vimpclust: Variable Importance in Clustering

An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	PCAmixdata, ggplot2, Polychrome, mclust, rlang
Suggests:	knitr, rmarkdown
Published:	2021-01-08
DOI:	10.32614/CRAN.package.vimpclust
Author:	Alex Mourer [aut], Marie Chavent [aut, ths], Madalina Olteanu [aut, ths, cre]
Maintainer:	Madalina Olteanu <madalina.olteanu at dauphine.psl.eu>
License:	GPL-3
NeedsCompilation:	no
CRAN checks:	vimpclust results

Documentation:

Reference manual:	vimpclust.pdf
Vignettes:	Group-sparse weighted k-means for numerical data Sparse weighted k-means for mixed data

Downloads:

Package source:	vimpclust_0.1.0.tar.gz
Windows binaries:	r-devel: vimpclust_0.1.0.zip, r-release: vimpclust_0.1.0.zip, r-oldrel: vimpclust_0.1.0.zip
macOS binaries:	r-release (arm64): vimpclust_0.1.0.tgz, r-oldrel (arm64): vimpclust_0.1.0.tgz, r-release (x86_64): vimpclust_0.1.0.tgz, r-oldrel (x86_64): vimpclust_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=vimpclust to link to this page.