Clustering gene expression data with the PKNNG metric
- Autores
- Bayá, Ariel E.; Granitto, Pablo Miguel
- Año de publicación
- 2008
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- In this work we use the recently introduced PKNNG metric, associated with a simple Hierarchical Clustering (HC) method, to find accurate an stable solution for the clustering of gene expression datasets. On real world problem it is important to evaluate the quality of the clustering proccess. According to this, we use a suitable framework to analyze the stability of the clustering solution obtained by HC + PKNNG. Using an artificial problem and two gene expression datasets, we show that the PKNNG metric gives better solutions than the Euclidean method and that those solutions are stable. Our results show the potential of the association of the PKNNG metric based clustering with the stability analysis for the class discovery process in high throughput data
Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Clustering
hierarchical clustering (HC)
PKNNG - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/21681
Ver los metadatos del registro completo
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Clustering gene expression data with the PKNNG metricBayá, Ariel E.Granitto, Pablo MiguelCiencias InformáticasClusteringhierarchical clustering (HC)PKNNGIn this work we use the recently introduced PKNNG metric, associated with a simple Hierarchical Clustering (HC) method, to find accurate an stable solution for the clustering of gene expression datasets. On real world problem it is important to evaluate the quality of the clustering proccess. According to this, we use a suitable framework to analyze the stability of the clustering solution obtained by HC + PKNNG. Using an artificial problem and two gene expression datasets, we show that the PKNNG metric gives better solutions than the Euclidean method and that those solutions are stable. Our results show the potential of the association of the PKNNG metric based clustering with the stability analysis for the class discovery process in high throughput dataWorkshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2008-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/21681enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:54:43Zoai:sedici.unlp.edu.ar:10915/21681Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:54:43.379SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Clustering gene expression data with the PKNNG metric |
title |
Clustering gene expression data with the PKNNG metric |
spellingShingle |
Clustering gene expression data with the PKNNG metric Bayá, Ariel E. Ciencias Informáticas Clustering hierarchical clustering (HC) PKNNG |
title_short |
Clustering gene expression data with the PKNNG metric |
title_full |
Clustering gene expression data with the PKNNG metric |
title_fullStr |
Clustering gene expression data with the PKNNG metric |
title_full_unstemmed |
Clustering gene expression data with the PKNNG metric |
title_sort |
Clustering gene expression data with the PKNNG metric |
dc.creator.none.fl_str_mv |
Bayá, Ariel E. Granitto, Pablo Miguel |
author |
Bayá, Ariel E. |
author_facet |
Bayá, Ariel E. Granitto, Pablo Miguel |
author_role |
author |
author2 |
Granitto, Pablo Miguel |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Clustering hierarchical clustering (HC) PKNNG |
topic |
Ciencias Informáticas Clustering hierarchical clustering (HC) PKNNG |
dc.description.none.fl_txt_mv |
In this work we use the recently introduced PKNNG metric, associated with a simple Hierarchical Clustering (HC) method, to find accurate an stable solution for the clustering of gene expression datasets. On real world problem it is important to evaluate the quality of the clustering proccess. According to this, we use a suitable framework to analyze the stability of the clustering solution obtained by HC + PKNNG. Using an artificial problem and two gene expression datasets, we show that the PKNNG metric gives better solutions than the Euclidean method and that those solutions are stable. Our results show the potential of the association of the PKNNG metric based clustering with the stability analysis for the class discovery process in high throughput data Workshop de Agentes y Sistemas Inteligentes (WASI) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
In this work we use the recently introduced PKNNG metric, associated with a simple Hierarchical Clustering (HC) method, to find accurate an stable solution for the clustering of gene expression datasets. On real world problem it is important to evaluate the quality of the clustering proccess. According to this, we use a suitable framework to analyze the stability of the clustering solution obtained by HC + PKNNG. Using an artificial problem and two gene expression datasets, we show that the PKNNG metric gives better solutions than the Euclidean method and that those solutions are stable. Our results show the potential of the association of the PKNNG metric based clustering with the stability analysis for the class discovery process in high throughput data |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-10 |
dc.type.none.fl_str_mv |
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 |
format |
conferenceObject |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/21681 |
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http://sedici.unlp.edu.ar/handle/10915/21681 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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