Metrics for Association Rule Clustering Assessment
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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:29462024-08-05T18:54:18.923445Repositório 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_ |
1808128996914757632 |