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A simple approach to sparse clustering

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  • Arias-Castro, Ery
  • Pu, Xiao

Abstract

Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others.

Suggested Citation

  • Arias-Castro, Ery & Pu, Xiao, 2017. "A simple approach to sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 217-228.
    • Handle: RePEc:eee:csdana:v:105:y:2017:i:c:p:217-228
    DOI: 10.1016/j.csda.2016.08.003
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    2. Chan, Yao-ban & Hall, Peter, 2010. "Using Evidence of Mixed Populations to Select Variables for Clustering Very High-Dimensional Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 798-809.
    3. Jian Guo & Elizaveta Levina & George Michailidis & Ji Zhu, 2010. "Pairwise Variable Selection for High-Dimensional Model-Based Clustering," Biometrics, The International Biometric Society, vol. 66(3), pages 793-804, September.
    4. Sijian Wang & Ji Zhu, 2008. "Variable Selection for Model-Based High-Dimensional Clustering and Its Application to Microarray Data," Biometrics, The International Biometric Society, vol. 64(2), pages 440-448, June.
    5. Raftery, Adrian E. & Dean, Nema, 2006. "Variable Selection for Model-Based Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 168-178, March.
    6. Jerome H. Friedman & Jacqueline J. Meulman, 2004. "Clustering objects on subsets of attributes (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 815-849, November.
    7. Witten, Daniela M. & Tibshirani, Robert, 2010. "A Framework for Feature Selection in Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 713-726.
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    Cited by:

    1. Banerjee, Trambak & Mukherjee, Gourab & Radchenko, Peter, 2017. "Feature screening in large scale cluster analysis," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 191-212.
    2. Ye, Mao & Zhang, Peng & Nie, Lizhen, 2018. "Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 32-49.

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