Variable selection for clustering with Gaussian mixture models: state of the art
Machine Learning
2017-02-01 v1 Machine Learning
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.
Cite
@article{arxiv.1701.08946,
title = {Variable selection for clustering with Gaussian mixture models: state of the art},
author = {Abdelghafour Talibi and Boujemâa Achchab and Rafik Lasri},
journal= {arXiv preprint arXiv:1701.08946},
year = {2017}
}