  The mixture models have become widely used in clustering, given its
probabilistic framework in which its based, however, for modern databases that
are characterized by their large size, these models behave disappointingly in
setting out the model, making essential the selection of relevant variables for
this type of clustering. After recalling the basics of clustering based on a
model, this article will examine the variable selection methods for model-based
clustering, as well as presenting opportunities for improvement of these
methods.
