Metrics for Association Rule Clustering Assessment

Detalhes bibliográficos
Autor(a) principal: Carvalho, Veronica Oliveira de [UNESP]
Data de Publicação: 2015
Outros Autores: Santos, Fabiano Fernandes dos, Rezende, Solange Oliveira
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://link.springer.com/chapter/10.1007%2F978-3-662-46335-2_5
http://hdl.handle.net/11449/129596
Resumo: Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. 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. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.
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spelling Metrics for Association Rule Clustering AssessmentAssociation rulesPre-processingClusteringEvaluation metricsIssues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. 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. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.Instituto de Geociências e Ciências Exatas, UNESP - Universidade Estadual Paulista, Rio Claro, BrazilInstituto de Ciências Matemáticas e de Computação, USP - Universidade de São Paulo, São Carlos, BrazilInstituto de Geociências e Ciências Exatas, UNESP - Universidade Estadual Paulista, Rio Claro, BrazilSpringerUniversidade Estadual Paulista (Unesp)Universidade de São Paulo (USP)Carvalho, Veronica Oliveira de [UNESP]Santos, Fabiano Fernandes dosRezende, Solange Oliveira2015-10-22T06:12:41Z2015-10-22T06:12:41Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject97-127http://link.springer.com/chapter/10.1007%2F978-3-662-46335-2_5Transactions On Large-scale Data- And Knowledge- Centered Systems Xvii. Berlin: Springer-verlag Berlin, v. 8970, p. 97-127, 2015.0302-9743http://hdl.handle.net/11449/12959610.1007/978-3-662-46335-2_5WOS:0003558145000051961581092362881Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengTransactions On Large-scale Data- And Knowledge- Centered Systems Xvii0,295info:eu-repo/semantics/openAccess2021-10-23T22:04:35Zoai:repositorio.unesp.br:11449/129596Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T22:04:35Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Metrics for Association Rule Clustering Assessment
title Metrics for Association Rule Clustering Assessment
spellingShingle Metrics for Association Rule Clustering Assessment
Carvalho, Veronica Oliveira de [UNESP]
Association rules
Pre-processing
Clustering
Evaluation metrics
title_short Metrics for Association Rule Clustering Assessment
title_full Metrics for Association Rule Clustering Assessment
title_fullStr Metrics for Association Rule Clustering Assessment
title_full_unstemmed Metrics for Association Rule Clustering Assessment
title_sort Metrics for Association Rule Clustering Assessment
author Carvalho, Veronica Oliveira de [UNESP]
author_facet Carvalho, Veronica Oliveira de [UNESP]
Santos, Fabiano Fernandes dos
Rezende, Solange Oliveira
author_role author
author2 Santos, Fabiano Fernandes dos
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 Carvalho, Veronica Oliveira de [UNESP]
Santos, Fabiano Fernandes dos
Rezende, Solange Oliveira
dc.subject.por.fl_str_mv Association rules
Pre-processing
Clustering
Evaluation metrics
topic Association rules
Pre-processing
Clustering
Evaluation metrics
description Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. 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. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.
publishDate 2015
dc.date.none.fl_str_mv 2015-10-22T06:12:41Z
2015-10-22T06:12:41Z
2015-01-01
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://link.springer.com/chapter/10.1007%2F978-3-662-46335-2_5
Transactions On Large-scale Data- And Knowledge- Centered Systems Xvii. Berlin: Springer-verlag Berlin, v. 8970, p. 97-127, 2015.
0302-9743
http://hdl.handle.net/11449/129596
10.1007/978-3-662-46335-2_5
WOS:000355814500005
1961581092362881
url http://link.springer.com/chapter/10.1007%2F978-3-662-46335-2_5
http://hdl.handle.net/11449/129596
identifier_str_mv Transactions On Large-scale Data- And Knowledge- Centered Systems Xvii. Berlin: Springer-verlag Berlin, v. 8970, p. 97-127, 2015.
0302-9743
10.1007/978-3-662-46335-2_5
WOS:000355814500005
1961581092362881
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Transactions On Large-scale Data- And Knowledge- Centered Systems Xvii
0,295
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 97-127
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv Web of Science
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_ 1803046692406165504

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