Metrics to support the evaluation of association rule clustering
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , |
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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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 |