Variable selection methods for model-based clustering
|Title:||Variable selection methods for model-based clustering||Authors:||Fop, Michael; Murphy, Thomas Brendan||Permanent link:||http://hdl.handle.net/10197/10520||Date:||26-Apr-2018||Online since:||2019-05-20T09:23:26Z||Abstract:||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||The American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and the Statistical Society of Canada||Journal:||Statistics Surveys||Volume:||12||Start page:||18||End page:||65||Copyright (published version):||2018 the Authors||Keywords:||Gaussian mixture model; Latent class analysis; Model-based clustering; R packages; Variable selection||DOI:||10.1214/18-SS119||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Insight Research Collection|
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