Interpretable clustering using unsupervised binary trees

Autores
Fraiman, Ricardo; Ghattas, Badih; Svarc, Marcela
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.
Fil: Fraiman, Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay
Fil: Ghattas, Badih. Université de la Méditerranée; Francia
Fil: Svarc, Marcela. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Unsupervised Classification
Cart
Pattern Recognition
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/27180
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Interpretable clustering using unsupervised binary treesFraiman, RicardoGhattas, BadihSvarc, MarcelaUnsupervised ClassificationCartPattern Recognitionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.Fil: Fraiman, Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; UruguayFil: Ghattas, Badih. Université de la Méditerranée; FranciaFil: Svarc, Marcela. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaSpringer2013-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/27180Fraiman, Ricardo; Ghattas, Badih; Svarc, Marcela; Interpretable clustering using unsupervised binary trees; Springer; Advances in Data Analysis and Classification; 7; 2; 3-2013; 125-1451862-53471862-5355CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11634-013-0129-3info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11634-013-0129-3info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1103.5339info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:11:37Zoai:ri.conicet.gov.ar:11336/27180instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:11:37.884CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Interpretable clustering using unsupervised binary trees
title Interpretable clustering using unsupervised binary trees
spellingShingle Interpretable clustering using unsupervised binary trees
Fraiman, Ricardo
Unsupervised Classification
Cart
Pattern Recognition
title_short Interpretable clustering using unsupervised binary trees
title_full Interpretable clustering using unsupervised binary trees
title_fullStr Interpretable clustering using unsupervised binary trees
title_full_unstemmed Interpretable clustering using unsupervised binary trees
title_sort Interpretable clustering using unsupervised binary trees
dc.creator.none.fl_str_mv Fraiman, Ricardo
Ghattas, Badih
Svarc, Marcela
author Fraiman, Ricardo
author_facet Fraiman, Ricardo
Ghattas, Badih
Svarc, Marcela
author_role author
author2 Ghattas, Badih
Svarc, Marcela
author2_role author
author
dc.subject.none.fl_str_mv Unsupervised Classification
Cart
Pattern Recognition
topic Unsupervised Classification
Cart
Pattern Recognition
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.
Fil: Fraiman, Ricardo. Universidad de San Andrés; Argentina. Universidad de la República; Uruguay
Fil: Ghattas, Badih. Université de la Méditerranée; Francia
Fil: Svarc, Marcela. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.
publishDate 2013
dc.date.none.fl_str_mv 2013-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/27180
Fraiman, Ricardo; Ghattas, Badih; Svarc, Marcela; Interpretable clustering using unsupervised binary trees; Springer; Advances in Data Analysis and Classification; 7; 2; 3-2013; 125-145
1862-5347
1862-5355
CONICET Digital
CONICET
url http://hdl.handle.net/11336/27180
identifier_str_mv Fraiman, Ricardo; Ghattas, Badih; Svarc, Marcela; Interpretable clustering using unsupervised binary trees; Springer; Advances in Data Analysis and Classification; 7; 2; 3-2013; 125-145
1862-5347
1862-5355
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s11634-013-0129-3
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11634-013-0129-3
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1103.5339
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/zip
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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