Fop, MichaelMichaelFopMurphy, Thomas BrendanThomas BrendanMurphy2019-05-202019-05-202018 the A2018-04-26Statistics Surveys1935-7516http://hdl.handle.net/10197/10520Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.enGaussian mixture modelLatent class analysisModel-based clusteringR packagesVariable selectionJournal Article12186510.1214/18-SS1192018-10-24SFI/12/RC/2289https://creativecommons.org/licenses/by-nc-nd/3.0/ie/