Subjective evaluation of labeling methods for 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/10.1007/978-3-642-45111-9_26 http://hdl.handle.net/11449/220069 |
Resumo: | Among the post-processing association rule approaches, clustering is an interesting one. When an association rule set is clustered, the user is provided with an improved presentation of the mined patters. The domain to be explored is structured aiming to join association rules with similar knowledge. To take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the association rule exploration process. Few works have explored and proposed labeling methods for this context. Moreover, these methods have not been explored through subjective evaluations in order to measure their quality; usually, only objective evaluations are used. This paper subjectively evaluates five labeling methods used on association rule clustering. The evaluation aims to find out the methods that presents the best results based on the analysis of the domain experts. The experimental results demonstrate that there is a disagreement between objective and subjective evaluations as reported in other works from literature. © Springer-Verlag 2013. |
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Repositório Institucional da UNESP |
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Subjective evaluation of labeling methods for association rule clusteringAmong the post-processing association rule approaches, clustering is an interesting one. When an association rule set is clustered, the user is provided with an improved presentation of the mined patters. The domain to be explored is structured aiming to join association rules with similar knowledge. To take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the association rule exploration process. Few works have explored and proposed labeling methods for this context. Moreover, these methods have not been explored through subjective evaluations in order to measure their quality; usually, only objective evaluations are used. This paper subjectively evaluates five labeling methods used on association rule clustering. The evaluation aims to find out the methods that presents the best results based on the analysis of the domain experts. The experimental results demonstrate that there is a disagreement between objective and subjective evaluations as reported in other works from literature. © Springer-Verlag 2013.Instituto de Ciências Matemáticas e de Computaçã o USP - Universidade de São Paulo, São CarlosInstituto de Geociências e Ciências Exatas UNESP - Univ Estadual Paulista, Rio ClaroInstituto de Geociências e Ciências Exatas UNESP - Univ Estadual Paulista, Rio ClaroUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)De Padua, RenanDos Santos, Fabiano FernandesDa Silva Conrado, MerleyDe Carvalho, Veronica Oliveira [UNESP]Rezende, Solange Oliveira2022-04-28T18:59:24Z2022-04-28T18:59:24Z2013-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject289-300http://dx.doi.org/10.1007/978-3-642-45111-9_26Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8266 LNAI, n. PART 2, p. 289-300, 2013.0302-97431611-3349http://hdl.handle.net/11449/22006910.1007/978-3-642-45111-9_262-s2.0-84893754595Scopusreponame: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)info:eu-repo/semantics/openAccess2022-04-28T18:59:24Zoai:repositorio.unesp.br:11449/220069Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:49:43.589468Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Subjective evaluation of labeling methods for association rule clustering |
title |
Subjective evaluation of labeling methods for association rule clustering |
spellingShingle |
Subjective evaluation of labeling methods for association rule clustering De Padua, Renan |
title_short |
Subjective evaluation of labeling methods for association rule clustering |
title_full |
Subjective evaluation of labeling methods for association rule clustering |
title_fullStr |
Subjective evaluation of labeling methods for association rule clustering |
title_full_unstemmed |
Subjective evaluation of labeling methods for association rule clustering |
title_sort |
Subjective evaluation of labeling methods for association rule clustering |
author |
De Padua, Renan |
author_facet |
De Padua, Renan Dos Santos, Fabiano Fernandes Da Silva Conrado, Merley De Carvalho, Veronica Oliveira [UNESP] Rezende, Solange Oliveira |
author_role |
author |
author2 |
Dos Santos, Fabiano Fernandes Da Silva Conrado, Merley De Carvalho, Veronica Oliveira [UNESP] Rezende, Solange Oliveira |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
De Padua, Renan Dos Santos, Fabiano Fernandes Da Silva Conrado, Merley De Carvalho, Veronica Oliveira [UNESP] Rezende, Solange Oliveira |
description |
Among the post-processing association rule approaches, clustering is an interesting one. When an association rule set is clustered, the user is provided with an improved presentation of the mined patters. The domain to be explored is structured aiming to join association rules with similar knowledge. To take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the association rule exploration process. Few works have explored and proposed labeling methods for this context. Moreover, these methods have not been explored through subjective evaluations in order to measure their quality; usually, only objective evaluations are used. This paper subjectively evaluates five labeling methods used on association rule clustering. The evaluation aims to find out the methods that presents the best results based on the analysis of the domain experts. The experimental results demonstrate that there is a disagreement between objective and subjective evaluations as reported in other works from literature. © Springer-Verlag 2013. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-12-01 2022-04-28T18:59:24Z 2022-04-28T18:59:24Z |
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/10.1007/978-3-642-45111-9_26 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8266 LNAI, n. PART 2, p. 289-300, 2013. 0302-9743 1611-3349 http://hdl.handle.net/11449/220069 10.1007/978-3-642-45111-9_26 2-s2.0-84893754595 |
url |
http://dx.doi.org/10.1007/978-3-642-45111-9_26 http://hdl.handle.net/11449/220069 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8266 LNAI, n. PART 2, p. 289-300, 2013. 0302-9743 1611-3349 10.1007/978-3-642-45111-9_26 2-s2.0-84893754595 |
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) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
289-300 |
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 |
|
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1808128864689324032 |