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

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Reference TypeJournal (article/letter/editorial)
TitleAssessing variable importance in clustering: a new method based on unsupervised binary decision trees
JournalComputational Statistics
AuthorsBadih, GhattasAuthor
Pierre, MichelAuthor
Laurent, BoyerAuthor
Year2019 (March)Volume34
Issue1
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s00180-018-0857-0Search in ResearchGate
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Mindat Ref. ID9546119Long-form Identifiermindat:1:5:9546119:5
GUID0
Full ReferenceBadih, Ghattas, Pierre, Michel, Laurent, Boyer (2019) Assessing variable importance in clustering: a new method based on unsupervised binary decision trees. Computational Statistics, 34 (1). 301-321 doi:10.1007/s00180-018-0857-0
Plain TextBadih, Ghattas, Pierre, Michel, Laurent, Boyer (2019) Assessing variable importance in clustering: a new method based on unsupervised binary decision trees. Computational Statistics, 34 (1). 301-321 doi:10.1007/s00180-018-0857-0
In(2019, March) Computational Statistics Vol. 34 (1) Springer Science and Business Media LLC

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Ghattas, Badih, Michel, Pierre, Boyer, Laurent (2017) Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methods. Pattern Recognition, 67. 177-185 doi:10.1016/j.patcog.2017.01.031
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Huang, Yu-Min (2019) Binary surrogates with stratified samples when weights are unknown. Computational Statistics, 34 (2). 653-682 doi:10.1007/s00180-018-0838-3
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