Metrics to support the evaluation of association rule clustering

Detalhes bibliográficos
Autor(a) principal: De Carvalho, Veronica Oliveira [UNESP]
Data de Publicação: 2013
Outros Autores: Dos Santos, Fabiano Fernandes, Rezende, Solange Oliveira
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-642-40131-2_21
http://hdl.handle.net/11449/76645
Resumo: Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.
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spelling Metrics to support the evaluation of association rule clusteringAssociation RulesClusteringPre-processingAssociation miningPre-processing stepResearch communitiesSuitable solutionsData warehousesAssociation rulesMany topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.Instituto de Geociências e Ciências Exatas UNESP - Univ. Estadual Paulista, Rio ClaroInstituto de Ciências Matemáticas e de Computaçã o USP - Universidade de São Paulo, São CarlosInstituto de Geociências e Ciências Exatas UNESP - Univ. Estadual Paulista, Rio ClaroUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)De Carvalho, Veronica Oliveira [UNESP]Dos Santos, Fabiano FernandesRezende, Solange Oliveira2014-05-27T11:30:45Z2014-05-27T11:30:45Z2013-09-26info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject248-259http://dx.doi.org/10.1007/978-3-642-40131-2_21Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259.0302-97431611-3349http://hdl.handle.net/11449/7664510.1007/978-3-642-40131-2_212-s2.0-84884493837Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)0,295info:eu-repo/semantics/openAccess2021-10-23T21:41:43Zoai:repositorio.unesp.br:11449/76645Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:03:45.198110Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Metrics to support the evaluation of association rule clustering
title Metrics to support the evaluation of association rule clustering
spellingShingle Metrics to support the evaluation of association rule clustering
De Carvalho, Veronica Oliveira [UNESP]
Association Rules
Clustering
Pre-processing
Association mining
Pre-processing step
Research communities
Suitable solutions
Data warehouses
Association rules
title_short Metrics to support the evaluation of association rule clustering
title_full Metrics to support the evaluation of association rule clustering
title_fullStr Metrics to support the evaluation of association rule clustering
title_full_unstemmed Metrics to support the evaluation of association rule clustering
title_sort Metrics to support the evaluation of association rule clustering
author De Carvalho, Veronica Oliveira [UNESP]
author_facet De Carvalho, Veronica Oliveira [UNESP]
Dos Santos, Fabiano Fernandes
Rezende, Solange Oliveira
author_role author
author2 Dos Santos, Fabiano Fernandes
Rezende, Solange Oliveira
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv De Carvalho, Veronica Oliveira [UNESP]
Dos Santos, Fabiano Fernandes
Rezende, Solange Oliveira
dc.subject.por.fl_str_mv Association Rules
Clustering
Pre-processing
Association mining
Pre-processing step
Research communities
Suitable solutions
Data warehouses
Association rules
topic Association Rules
Clustering
Pre-processing
Association mining
Pre-processing step
Research communities
Suitable solutions
Data warehouses
Association rules
description Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.
publishDate 2013
dc.date.none.fl_str_mv 2013-09-26
2014-05-27T11:30:45Z
2014-05-27T11:30:45Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-642-40131-2_21
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259.
0302-9743
1611-3349
http://hdl.handle.net/11449/76645
10.1007/978-3-642-40131-2_21
2-s2.0-84884493837
url http://dx.doi.org/10.1007/978-3-642-40131-2_21
http://hdl.handle.net/11449/76645
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259.
0302-9743
1611-3349
10.1007/978-3-642-40131-2_21
2-s2.0-84884493837
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 248-259
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128310243229696

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