Variable selection for clustering with Gaussian mixture models: state of the art

TL;DR

This paper reviews current methods for variable selection in Gaussian mixture model-based clustering, highlighting challenges and potential improvements for handling large modern datasets.

Contribution

It provides a comprehensive overview of existing variable selection techniques in model-based clustering and discusses avenues for enhancing these methods.

Findings

Identifies limitations of current variable selection methods in large datasets.

Discusses potential improvements for model-based clustering variable selection.

Highlights the importance of relevant variable selection for clustering accuracy.

Abstract

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.