Skip to Main content Skip to Navigation
Journal articles

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

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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal-amu.archives-ouvertes.fr/hal-02007388
Contributor : Elisabeth Lhuillier <>
Submitted on : Wednesday, May 13, 2020 - 6:01:27 PM
Last modification on : Monday, May 18, 2020 - 10:45:45 AM

File

Ghattas2019_Article_AssessingV...
Files produced by the author(s)

Identifiers

Collections

Citation

Ghattas Badih, Michel Pierre, Boyer Laurent. Assessing variable importance in clustering: a new method based on unsupervised binary decision trees. Computational Statistics, Springer Verlag, 2019, 34 (1), pp.301-321. ⟨10.1007/s00180-018-0857-0⟩. ⟨hal-02007388⟩

Share

Metrics

Record views

132

Files downloads

96