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vimpclust  

Variable Importance in Clustering
View on CRAN: Click here


Download and install vimpclust package within the R console
Install from CRAN:
install.packages("vimpclust")

Install from Github:
library("remotes")
install_github("cran/vimpclust")

Install by package version:
library("remotes")
install_version("vimpclust", "0.1.0")



Attach the package and use:
library("vimpclust")
Maintained by
Madalina Olteanu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-01-08
Latest Update: 2021-01-08
Description:
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>.
How to cite:
Madalina Olteanu (2021). vimpclust: Variable Importance in Clustering. R package version 0.1.0, https://cran.r-project.org/web/packages/vimpclust. Accessed 05 Jun. 2026.
Previous versions and publish date:
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Complete documentation for vimpclust
Functions, R codes and Examples using the vimpclust R package
Some associated functions: DataMice . HDdata . check_fun_groupsparsw . groupsoft . groupsparsewkm . info_clust . plot.spwkm . recodmix . sparsewkm . weightedss . 
Some associated R codes: DataMice.R . HDdata.R . checkingconditions.R . groupsoft.R . groupsparsewkm.R . info_clust.R . plot.spwkm.R . recodmix.R . sparsewkm.R . weightedss.R .  Full vimpclust package functions and examples
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