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

Badih Ghattas, Pierre Michel () and Boyer Laurent ()
Additional contact information
Boyer Laurent: Aix Marseille Université

Computational Statistics, 2019, vol. 34, issue 1, No 13, 321 pages

Abstract: 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.

Keywords: Unsupervised learning; CUBT; Deviance; Variable importance; Variables ranking (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-018-0857-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:34:y:2019:i:1:d:10.1007_s00180-018-0857-0

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-018-0857-0

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().