  Model-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.
