Variable selection methods for model-based clustering

Michael Fop, Thomas Brendan Murphy
<span title="">2018</span> <i title="Institute of Mathematical Statistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xzqhypwcw5bejeveuwrl7y7nli" style="color: black;">Statistics Survey</a> </i> &nbsp;
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
more &raquo; ... terpretation 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1214/18-ss119">doi:10.1214/18-ss119</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lozihfml3razfjz7lgvp7oo2bi">fatcat:lozihfml3razfjz7lgvp7oo2bi</a> </span>
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