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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Michael Fop
- sl:arxiv_num : 1707.00306
- sl:arxiv_published : 2017-07-02T15:29:13Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Variable Selection Methods for Model-based Clustering@en
- sl:arxiv_updated : 2018-06-04T07:52:56Z
- sl:bookmarkOf : https://arxiv.org/abs/1707.00306
- sl:creationDate : 2019-12-11
- sl:creationTime : 2019-12-11T03:15:56Z
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