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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/76220
Ver los metadatos del registro completo
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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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info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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eng |
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http://creativecommons.org/licenses/by-sa/4.0/ Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) |
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