Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: clustvarsel: A Package Implementing Variable Selection for Model-based
  Clustering in R   Finite mixture modelling provides a framework for cluster analysis based on
parsimonious Gaussian mixture models. Variable or feature selection is of
particular importance in situations where only a subset of the available
variables provide clustering information. This enables the selection of a more
parsimonious model, yielding more efficient estimates, a clearer interpretation
and, often, improved clustering partitions. This paper describes the R package
clustvarsel which performs subset selection for model-based clustering. An
improved version of the methodology of Raftery and Dean (2006) is implemented
in the new version 2 of the package to find the (locally) optimal subset of
variables with group/cluster information in a dataset. Search over the solution
space is performed using either a stepwise greedy search or a headlong
algorithm. Adjustments for speeding up these algorithms are discussed, as well
as a parallel implementation of the stepwise search. Usage of the package is
presented through the discussion of several data examples.

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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn