A Novel Method to Control the Diversity in Cluster Ensembles

Autores
Pividori, Milton; Stegmayer, Georgina; Milone, Diego H.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
consensus clustering
ensemble diversity
cluster ensemble generation
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/76220

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spelling A Novel Method to Control the Diversity in Cluster EnsemblesPividori, MiltonStegmayer, GeorginaMilone, Diego H.Ciencias Informáticasconsensus clusteringensemble diversitycluster ensemble generationClustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.Sociedad Argentina de Informática e Investigación Operativa2013-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf121-132http://sedici.unlp.edu.ar/handle/10915/76220enginfo:eu-repo/semantics/altIdentifier/url/http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/ASAI/11.pdfinfo:eu-repo/semantics/altIdentifier/issn/1850-2784info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:13:24Zoai:sedici.unlp.edu.ar:10915/76220Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:13:24.72SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A Novel Method to Control the Diversity in Cluster Ensembles
title A Novel Method to Control the Diversity in Cluster Ensembles
spellingShingle A Novel Method to Control the Diversity in Cluster Ensembles
Pividori, Milton
Ciencias Informáticas
consensus clustering
ensemble diversity
cluster ensemble generation
title_short A Novel Method to Control the Diversity in Cluster Ensembles
title_full A Novel Method to Control the Diversity in Cluster Ensembles
title_fullStr A Novel Method to Control the Diversity in Cluster Ensembles
title_full_unstemmed A Novel Method to Control the Diversity in Cluster Ensembles
title_sort A Novel Method to Control the Diversity in Cluster Ensembles
dc.creator.none.fl_str_mv Pividori, Milton
Stegmayer, Georgina
Milone, Diego H.
author Pividori, Milton
author_facet Pividori, Milton
Stegmayer, Georgina
Milone, Diego H.
author_role author
author2 Stegmayer, Georgina
Milone, Diego H.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
consensus clustering
ensemble diversity
cluster ensemble generation
topic Ciencias Informáticas
consensus clustering
ensemble diversity
cluster ensemble generation
dc.description.none.fl_txt_mv Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.
Sociedad Argentina de Informática e Investigación Operativa
description Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. Although disagreement among ensemble partitions (diversity) has been found to be fundamental for success, the literature has arrived to confusing conclusions: some authors suggest that high diversity is beneficial for the final performance, whereas others have indicated that medium is better. While there are several options to measure the diversity, there is no method to control it. This paper introduces a new ensemble generation strategy and a method to smoothly change the ensemble diversity. Experimental results on three datasets suggest that this is an important step towards a more systematic approach to analyze the impact of the ensemble diversity on the overall consensus performance.
publishDate 2013
dc.date.none.fl_str_mv 2013-09
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