Subjective evaluation of labeling methods for association rule clustering

Detalhes bibliográficos
Autor(a) principal: De Padua, Renan
Data de Publicação: 2013
Outros Autores: Dos Santos, Fabiano Fernandes, Da Silva Conrado, Merley, De Carvalho, Veronica Oliveira [UNESP], Rezende, Solange Oliveira
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-642-45111-9_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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spelling 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
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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
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dc.format.none.fl_str_mv 289-300
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
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