Knowledge discovery based on computational taxonomy and intelligent data mining

Autores
Perichinsky, Gregorio; García Martínez, Ramón; Proto, Araceli
Año de publicación
2000
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use of both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but also for guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.
Área: Ingeniería de Software - Bases de Datos
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Data mining
Clustering
knowledge discovery
insufficient database
taxonomy
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:10915/23755
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23755
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Knowledge discovery based on computational taxonomy and intelligent data miningPerichinsky, GregorioGarcía Martínez, RamónProto, AraceliCiencias InformáticasData miningClusteringknowledge discoveryinsufficient databasetaxonomyThis study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use of both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but also for guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.Área: Ingeniería de Software - Bases de DatosRed de Universidades con Carreras en Informática (RedUNCI)2000-10info: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/10915/23755enginfo: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:UNLP2025-09-17T09:38:59Zoai:sedici.unlp.edu.ar:10915/23755Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-17 09:38:59.609SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Knowledge discovery based on computational taxonomy and intelligent data mining
title Knowledge discovery based on computational taxonomy and intelligent data mining
spellingShingle Knowledge discovery based on computational taxonomy and intelligent data mining
Perichinsky, Gregorio
Ciencias Informáticas
Data mining
Clustering
knowledge discovery
insufficient database
taxonomy
title_short Knowledge discovery based on computational taxonomy and intelligent data mining
title_full Knowledge discovery based on computational taxonomy and intelligent data mining
title_fullStr Knowledge discovery based on computational taxonomy and intelligent data mining
title_full_unstemmed Knowledge discovery based on computational taxonomy and intelligent data mining
title_sort Knowledge discovery based on computational taxonomy and intelligent data mining
dc.creator.none.fl_str_mv Perichinsky, Gregorio
García Martínez, Ramón
Proto, Araceli
author Perichinsky, Gregorio
author_facet Perichinsky, Gregorio
García Martínez, Ramón
Proto, Araceli
author_role author
author2 García Martínez, Ramón
Proto, Araceli
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Data mining
Clustering
knowledge discovery
insufficient database
taxonomy
topic Ciencias Informáticas
Data mining
Clustering
knowledge discovery
insufficient database
taxonomy
dc.description.none.fl_txt_mv This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use of both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but also for guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.
Área: Ingeniería de Software - Bases de Datos
Red de Universidades con Carreras en Informática (RedUNCI)
description This study investigates an approach of knowledge discovery and data mining in insufficient databases. An application of Computational Taxonomy analysis demonstrates that the approach is effective in such a data mining process. The approach is characterized by the use of both the second type of domain knowledge and visualization. This type of knowledge is newly defined in this study and deduced from supposition about background situations of the domain. The supposition is triggered by strong intuition about the extracted features in a recurrent process of data mining. This type of domain knowledge is useful not only for discovering interesting knowledge but also for guiding the subsequent search for more explicit and interesting knowledge. The visualization is very useful for triggering the supposition.
publishDate 2000
dc.date.none.fl_str_mv 2000-10
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/10915/23755
url http://sedici.unlp.edu.ar/handle/10915/23755
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
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score 13.000565

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