Diversity control for improving the analysis of consensus clustering

Autores
Pividori, Milton Damián; Stegmayer, Georgina; Milone, Diego Humberto
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated solution (consensus partition) that outperforms all of the members of the input set. The diversity of ensemble partitions has been found to be a key aspect for obtaining good results, but the conclusions of previous studies are contradictory. Therefore, ensemble diversity analysis is currently an important issue because there are no methods for smoothly changing the diversity of an ensemble, which makes it very difficult to study the impact of ensemble diversity on consensus results. Indeed, ensembles with similar diversity can have very different properties, thereby producing a consensus function with unpredictable behavior. In this study, we propose a novel method for increasing and decreasing the diversity of data partitions in a smooth manner by adjusting a single parameter, thereby achieving fine-grained control of ensemble diversity. The results obtained using well-known data sets indicate that the proposed method is effective for controlling the dissimilarity among ensemble members to obtain a consensus function with smooth behavior. This method is important for facilitating the analysis of the impact of ensemble diversity in consensus clustering.
Fil: Pividori, Milton Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Materia
Cluster Ensembles
Consensus Clustering
Diversity Analysis
Diversity Control
Ensemble Diversity
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/47804
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Diversity control for improving the analysis of consensus clusteringPividori, Milton DamiánStegmayer, GeorginaMilone, Diego HumbertoCluster EnsemblesConsensus ClusteringDiversity AnalysisDiversity ControlEnsemble Diversityhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated solution (consensus partition) that outperforms all of the members of the input set. The diversity of ensemble partitions has been found to be a key aspect for obtaining good results, but the conclusions of previous studies are contradictory. Therefore, ensemble diversity analysis is currently an important issue because there are no methods for smoothly changing the diversity of an ensemble, which makes it very difficult to study the impact of ensemble diversity on consensus results. Indeed, ensembles with similar diversity can have very different properties, thereby producing a consensus function with unpredictable behavior. In this study, we propose a novel method for increasing and decreasing the diversity of data partitions in a smooth manner by adjusting a single parameter, thereby achieving fine-grained control of ensemble diversity. The results obtained using well-known data sets indicate that the proposed method is effective for controlling the dissimilarity among ensemble members to obtain a consensus function with smooth behavior. This method is important for facilitating the analysis of the impact of ensemble diversity in consensus clustering.Fil: Pividori, Milton Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaElsevier Science Inc2016-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/47804Pividori, Milton Damián; Stegmayer, Georgina; Milone, Diego Humberto; Diversity control for improving the analysis of consensus clustering; Elsevier Science Inc; Information Sciences; 361-362; 9-2016; 120-1340020-0255CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0020025516302705info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2016.04.027info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:11:07Zoai:ri.conicet.gov.ar:11336/47804instacron: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:34982025-09-29 10:11:08.041CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Diversity control for improving the analysis of consensus clustering
title Diversity control for improving the analysis of consensus clustering
spellingShingle Diversity control for improving the analysis of consensus clustering
Pividori, Milton Damián
Cluster Ensembles
Consensus Clustering
Diversity Analysis
Diversity Control
Ensemble Diversity
title_short Diversity control for improving the analysis of consensus clustering
title_full Diversity control for improving the analysis of consensus clustering
title_fullStr Diversity control for improving the analysis of consensus clustering
title_full_unstemmed Diversity control for improving the analysis of consensus clustering
title_sort Diversity control for improving the analysis of consensus clustering
dc.creator.none.fl_str_mv Pividori, Milton Damián
Stegmayer, Georgina
Milone, Diego Humberto
author Pividori, Milton Damián
author_facet Pividori, Milton Damián
Stegmayer, Georgina
Milone, Diego Humberto
author_role author
author2 Stegmayer, Georgina
Milone, Diego Humberto
author2_role author
author
dc.subject.none.fl_str_mv Cluster Ensembles
Consensus Clustering
Diversity Analysis
Diversity Control
Ensemble Diversity
topic Cluster Ensembles
Consensus Clustering
Diversity Analysis
Diversity Control
Ensemble Diversity
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated solution (consensus partition) that outperforms all of the members of the input set. The diversity of ensemble partitions has been found to be a key aspect for obtaining good results, but the conclusions of previous studies are contradictory. Therefore, ensemble diversity analysis is currently an important issue because there are no methods for smoothly changing the diversity of an ensemble, which makes it very difficult to study the impact of ensemble diversity on consensus results. Indeed, ensembles with similar diversity can have very different properties, thereby producing a consensus function with unpredictable behavior. In this study, we propose a novel method for increasing and decreasing the diversity of data partitions in a smooth manner by adjusting a single parameter, thereby achieving fine-grained control of ensemble diversity. The results obtained using well-known data sets indicate that the proposed method is effective for controlling the dissimilarity among ensemble members to obtain a consensus function with smooth behavior. This method is important for facilitating the analysis of the impact of ensemble diversity in consensus clustering.
Fil: Pividori, Milton Damián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; Argentina
Fil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
description Consensus clustering has emerged as a powerful technique for obtaining better clustering results, where a set of data partitions (ensemble) are generated, which are then combined to obtain a consolidated solution (consensus partition) that outperforms all of the members of the input set. The diversity of ensemble partitions has been found to be a key aspect for obtaining good results, but the conclusions of previous studies are contradictory. Therefore, ensemble diversity analysis is currently an important issue because there are no methods for smoothly changing the diversity of an ensemble, which makes it very difficult to study the impact of ensemble diversity on consensus results. Indeed, ensembles with similar diversity can have very different properties, thereby producing a consensus function with unpredictable behavior. In this study, we propose a novel method for increasing and decreasing the diversity of data partitions in a smooth manner by adjusting a single parameter, thereby achieving fine-grained control of ensemble diversity. The results obtained using well-known data sets indicate that the proposed method is effective for controlling the dissimilarity among ensemble members to obtain a consensus function with smooth behavior. This method is important for facilitating the analysis of the impact of ensemble diversity in consensus clustering.
publishDate 2016
dc.date.none.fl_str_mv 2016-09
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/11336/47804
Pividori, Milton Damián; Stegmayer, Georgina; Milone, Diego Humberto; Diversity control for improving the analysis of consensus clustering; Elsevier Science Inc; Information Sciences; 361-362; 9-2016; 120-134
0020-0255
CONICET Digital
CONICET
url http://hdl.handle.net/11336/47804
identifier_str_mv Pividori, Milton Damián; Stegmayer, Georgina; Milone, Diego Humberto; Diversity control for improving the analysis of consensus clustering; Elsevier Science Inc; Information Sciences; 361-362; 9-2016; 120-134
0020-0255
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0020025516302705
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ins.2016.04.027
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
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 13.070432

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