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Assessing variable importance in clustering: a new method based on unsupervised binary decision trees

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  • Ghattas Badih

    (Aix Marseille Université, CNRS, Centrale Marseille)

  • Michel Pierre

    (Aix Marseille Université, CNRS, Centrale Marseille
    Aix Marseille Université)

  • Boyer Laurent

    (Aix Marseille Université)

Abstract

We consider different approaches for assessing variable importance in clustering. We focus on clustering using binary decision trees (CUBT), which is a non-parametric top-down hierarchical clustering method designed for both continuous and nominal data. We suggest a measure of variable importance for this method similar to the one used in Breiman’s classification and regression trees. This score is useful to rank the variables in a dataset, to determine which variables are the most important or to detect the irrelevant ones. We analyze both stability and efficiency of this score on different data simulation models in the presence of noise, and compare it to other classical variable importance measures. Our experiments show that variable importance based on CUBT is much more efficient than other approaches in a large variety of situations.

Suggested Citation

  • Ghattas Badih & Michel Pierre & Boyer Laurent, 2019. "Assessing variable importance in clustering: a new method based on unsupervised binary decision trees," Computational Statistics, Springer, vol. 34(1), pages 301-321, March.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:1:d:10.1007_s00180-018-0857-0
    DOI: 10.1007/s00180-018-0857-0
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    1. R. Darrell Bock, 1972. "Estimating item parameters and latent ability when responses are scored in two or more nominal categories," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 29-51, March.
    2. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," 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. 7(2), pages 125-145, June.
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