Variable Importance in Clustering
check_fun_groupsparsw
Group soft-thresholding operator
Group-sparse weighted k-means
Description of a set of partitions
Plots from a "spwkm" object
Recoding mixed data
Sparse weighted k-means
Weighted sum-of-squares criteria
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>.