- Statist. Surv.
- Volume 12 (2018), 18-65.
Variable selection methods for model-based clustering
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.
Statist. Surv., Volume 12 (2018), 18-65.
Received: July 2017
First available in Project Euclid: 26 April 2018
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Fop, Michael; Murphy, Thomas Brendan. Variable selection methods for model-based clustering. Statist. Surv. 12 (2018), 18--65. doi:10.1214/18-SS119. https://projecteuclid.org/euclid.ssu/1524729611