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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/21681

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network_name_str SEDICI (UNLP)
spelling 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
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dc.language.none.fl_str_mv eng
language eng
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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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rights_invalid_str_mv 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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