Biclustering in data mining using a memetic multi-objective evolutionary algorithm

Autores
Gallo, Cristian Andrés; Maguitman, Ana Gabriela; Carballido, Jessica Andrea; Ponzoni, Ignacio
Año de publicación
2008
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this paper, a new memetic strategy that integrates a multi-objective evolutionary algorithm (the SPEA2) with a local search technique for data mining is presented. The algorithm explores a Term Frequency-Inverse Document Frequency (TF-IDF) data matrix in order to find biclusters that fulfill several objectives. The case of study was a dataset corresponding to the Reuters-21578 corpus. Our algorithm performed satisfactorily, finding biclusters that have large size and coherent values, yielding to undeniably promising outcomes. Nonetheless, more experiments with data from other corpus are necessary, thus leading to more concluding results
Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
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:10915/21682
Acceder
id SEDICI_6ce57b70a678a86eaf946e00899afc18
oai_identifier_str oai:sedici.unlp.edu.ar:10915/21682
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Biclustering in data mining using a memetic multi-objective evolutionary algorithmGallo, Cristian AndrésMaguitman, Ana GabrielaCarballido, Jessica AndreaPonzoni, IgnacioCiencias Informáticasbiclusteringevolutionary algorithmsData miningAlgorithmsIn this paper, a new memetic strategy that integrates a multi-objective evolutionary algorithm (the SPEA2) with a local search technique for data mining is presented. The algorithm explores a Term Frequency-Inverse Document Frequency (TF-IDF) data matrix in order to find biclusters that fulfill several objectives. The case of study was a dataset corresponding to the Reuters-21578 corpus. Our algorithm performed satisfactorily, finding biclusters that have large size and coherent values, yielding to undeniably promising outcomes. Nonetheless, more experiments with data from other corpus are necessary, thus leading to more concluding resultsWorkshop 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/10915/21682enginfo: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:UNLP2025-10-15T10:47:19Zoai:sedici.unlp.edu.ar:10915/21682Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:20.269SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title Biclustering in data mining using a memetic multi-objective evolutionary algorithm
spellingShingle Biclustering in data mining using a memetic multi-objective evolutionary algorithm
Gallo, Cristian Andrés
Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
title_short Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_full Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_fullStr Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_full_unstemmed Biclustering in data mining using a memetic multi-objective evolutionary algorithm
title_sort Biclustering in data mining using a memetic multi-objective evolutionary algorithm
dc.creator.none.fl_str_mv Gallo, Cristian Andrés
Maguitman, Ana Gabriela
Carballido, Jessica Andrea
Ponzoni, Ignacio
author Gallo, Cristian Andrés
author_facet Gallo, Cristian Andrés
Maguitman, Ana Gabriela
Carballido, Jessica Andrea
Ponzoni, Ignacio
author_role author
author2 Maguitman, Ana Gabriela
Carballido, Jessica Andrea
Ponzoni, Ignacio
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
topic Ciencias Informáticas
biclustering
evolutionary algorithms
Data mining
Algorithms
dc.description.none.fl_txt_mv In this paper, a new memetic strategy that integrates a multi-objective evolutionary algorithm (the SPEA2) with a local search technique for data mining is presented. The algorithm explores a Term Frequency-Inverse Document Frequency (TF-IDF) data matrix in order to find biclusters that fulfill several objectives. The case of study was a dataset corresponding to the Reuters-21578 corpus. Our algorithm performed satisfactorily, finding biclusters that have large size and coherent values, yielding to undeniably promising outcomes. Nonetheless, more experiments with data from other corpus are necessary, thus leading to more concluding results
Workshop de Agentes y Sistemas Inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description In this paper, a new memetic strategy that integrates a multi-objective evolutionary algorithm (the SPEA2) with a local search technique for data mining is presented. The algorithm explores a Term Frequency-Inverse Document Frequency (TF-IDF) data matrix in order to find biclusters that fulfill several objectives. The case of study was a dataset corresponding to the Reuters-21578 corpus. Our algorithm performed satisfactorily, finding biclusters that have large size and coherent values, yielding to undeniably promising outcomes. Nonetheless, more experiments with data from other corpus are necessary, thus leading to more concluding results
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
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21682
url http://sedici.unlp.edu.ar/handle/10915/21682
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)
eu_rights_str_mv openAccess
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)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846063899272544256
score 13.22299

Publicaciones similares