Contrast set mining in temporal databases

Detalhes bibliográficos
Autor(a) principal: Magalhães, André
Data de Publicação: 2015
Outros Autores: Azevedo, Paulo J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/33862
Resumo: Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute–value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.
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spelling Contrast set mining in temporal databasesSoftware engineeringArtificial intelligenceKnowledge acquisitionKnowledge representationKnowledge base systemknowledge base < systemknowledge base &lt; systemScience & TechnologyUnderstanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute–value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.(undefined)WileyElsevierUniversidade do MinhoMagalhães, AndréAzevedo, Paulo J.20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/33862engMagalhaes, A., & Azevedo, P. J. (2015). Contrast set mining in temporal databases. Expert Systems, 32(3), 435-443. doi: 10.1111/exsy.120801468-039410.1111/exsy.12080info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:23:27Zoai:repositorium.sdum.uminho.pt:1822/33862Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:17:10.183734Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Contrast set mining in temporal databases
title Contrast set mining in temporal databases
spellingShingle Contrast set mining in temporal databases
Magalhães, André
Software engineering
Artificial intelligence
Knowledge acquisition
Knowledge representation
Knowledge base system
knowledge base < system
knowledge base &lt; system
Science & Technology
title_short Contrast set mining in temporal databases
title_full Contrast set mining in temporal databases
title_fullStr Contrast set mining in temporal databases
title_full_unstemmed Contrast set mining in temporal databases
title_sort Contrast set mining in temporal databases
author Magalhães, André
author_facet Magalhães, André
Azevedo, Paulo J.
author_role author
author2 Azevedo, Paulo J.
author2_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Magalhães, André
Azevedo, Paulo J.
dc.subject.por.fl_str_mv Software engineering
Artificial intelligence
Knowledge acquisition
Knowledge representation
Knowledge base system
knowledge base < system
knowledge base &lt; system
Science & Technology
topic Software engineering
Artificial intelligence
Knowledge acquisition
Knowledge representation
Knowledge base system
knowledge base < system
knowledge base &lt; system
Science & Technology
description Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute–value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/33862
url http://hdl.handle.net/1822/33862
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Magalhaes, A., & Azevedo, P. J. (2015). Contrast set mining in temporal databases. Expert Systems, 32(3), 435-443. doi: 10.1111/exsy.12080
1468-0394
10.1111/exsy.12080
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Wiley
Elsevier
publisher.none.fl_str_mv Wiley
Elsevier
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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