Assessing variable importance in clustering: a new method based on unsupervised binary decision trees
type: Item
instance of: scholarly article
title: Assessing variable importance in clustering: a new method based on unsupervised binary decision trees
zbMATH Open document ID: 1417.62168
author: Ghattas Badih
author: Michel Pierre
author: Boyer Laurent
published in: Computational Statistics
publication date: 2019-02-27T00:00:00Z
full work available at URL: Ghattas2019 Article AssessingVariableImportanceInC.pdf
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62-08
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7030233
zbMATH Keywords: unsupervised learning
zbMATH Keywords: CUBT
zbMATH Keywords: deviance
zbMATH Keywords: variable importance
zbMATH Keywords: variables ranking
describes a project that uses: ltm
describes a project that uses: randomForest
describes a project that uses: R
describes a project that uses: wskm
describes a project that uses: mcIRT
MaRDI profile type: Publication
OpenAlex ID: W2906948823
cites work: Estimating item parameters and latent ability when responses are scored in two or more nominal categories
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cites work: Random forests
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cites work: Q5617409
cites work: 10.1162/153244303322753706
cites work: 10.1162/153244303322753751
DOI: 10.1007/S00180-018-0857-0
Wikidata QID: Q128645177
description: scientific article; zbMATH DE number 7030233