CLUIN – A new method for extracting rules for large databases

Autores
Hasperué, Waldo; Corbalán, Leonardo César
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, the CLUHR classification strategy is extended to work with databases that have nominal attributes. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Rule extraction
classification
large datasets
supervised learning
Intelligent agents
base de datos
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:10915/23603
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23603
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling CLUIN – A new method for extracting rules for large databasesHasperué, WaldoCorbalán, Leonardo CésarCiencias InformáticasRule extractionclassificationlarge datasetssupervised learningIntelligent agentsbase de datosWhen there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, the CLUHR classification strategy is extended to work with databases that have nominal attributes. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2012-10info: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/10915/23603enginfo: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:UNLP2025-09-29T10:55:31Zoai:sedici.unlp.edu.ar:10915/23603Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:31.669SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv CLUIN – A new method for extracting rules for large databases
title CLUIN – A new method for extracting rules for large databases
spellingShingle CLUIN – A new method for extracting rules for large databases
Hasperué, Waldo
Ciencias Informáticas
Rule extraction
classification
large datasets
supervised learning
Intelligent agents
base de datos
title_short CLUIN – A new method for extracting rules for large databases
title_full CLUIN – A new method for extracting rules for large databases
title_fullStr CLUIN – A new method for extracting rules for large databases
title_full_unstemmed CLUIN – A new method for extracting rules for large databases
title_sort CLUIN – A new method for extracting rules for large databases
dc.creator.none.fl_str_mv Hasperué, Waldo
Corbalán, Leonardo César
author Hasperué, Waldo
author_facet Hasperué, Waldo
Corbalán, Leonardo César
author_role author
author2 Corbalán, Leonardo César
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Rule extraction
classification
large datasets
supervised learning
Intelligent agents
base de datos
topic Ciencias Informáticas
Rule extraction
classification
large datasets
supervised learning
Intelligent agents
base de datos
dc.description.none.fl_txt_mv When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, the CLUHR classification strategy is extended to work with databases that have nominal attributes. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, the CLUHR classification strategy is extended to work with databases that have nominal attributes. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
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
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format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23603
url http://sedici.unlp.edu.ar/handle/10915/23603
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
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reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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