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High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust

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  • Nia, Vahid Partovi
  • Davison, Anthony C.

Abstract

The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, thereby yielding a fully automatic method. We discuss computational problems arising in implementation of the procedure and illustrate the usefulness of the package through examples.

Suggested Citation

  • Nia, Vahid Partovi & Davison, Anthony C., 2012. "High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i05).
    • Handle: RePEc:jss:jstsof:v:047:i05
    DOI: http://hdl.handle.net/10.18637/jss.v047.i05
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    References listed on IDEAS

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    1. Bergé, Laurent & Bouveyron, Charles & Girard, Stéphane, 2012. "HDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 46(i06).
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    Cited by:

    1. Gilles Celeux & Cathy Maugis-Rabusseau & Mohammed Sedki, 2019. "Variable selection in model-based clustering and discriminant analysis with a regularization approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 259-278, March.
    2. Bouveyron, Charles & Brunet-Saumard, Camille, 2014. "Model-based clustering of high-dimensional data: A review," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 52-78.

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