Semi-supervised learning to support the exploration of association rules
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-10160-6_40 http://hdl.handle.net/11449/167656 |
Resumo: | In the last years, many approaches for post-processing association rules have been proposed. The automatics are simple to use, but they don't consider users' subjectivity. Unlike, the approaches that consider subjectivity need an explicit description of the users' knowledge and/or interests, requiring a considerable time from the user. Looking at the problem from another perspective, post-processing can be seen as a classification task, in which the user labels some rules as interesting [I] or not interesting [NI], for example, in order to propagate these labels to the other unlabeled rules. This work presents a framework for post-processing association rules that uses semi-supervised learning in which: (a) the user is constantly directed to the [I] patterns of the domain, minimizing his exploration effort by reducing the exploration space, since his knowledge and/or interests are iteratively propagated; (b) the users' subjectivity is considered without using any formalism, making the task simpler. © 2014 Springer International Publishing. |
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Repositório Institucional da UNESP |
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Semi-supervised learning to support the exploration of association rulesAssociation RulesPost-processingSemi-supervised Learning (SSL)In the last years, many approaches for post-processing association rules have been proposed. The automatics are simple to use, but they don't consider users' subjectivity. Unlike, the approaches that consider subjectivity need an explicit description of the users' knowledge and/or interests, requiring a considerable time from the user. Looking at the problem from another perspective, post-processing can be seen as a classification task, in which the user labels some rules as interesting [I] or not interesting [NI], for example, in order to propagate these labels to the other unlabeled rules. This work presents a framework for post-processing association rules that uses semi-supervised learning in which: (a) the user is constantly directed to the [I] patterns of the domain, minimizing his exploration effort by reducing the exploration space, since his knowledge and/or interests are iteratively propagated; (b) the users' subjectivity is considered without using any formalism, making the task simpler. © 2014 Springer International Publishing.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]De Padua, RenanRezende, Solange Oliveira2018-12-11T16:37:48Z2018-12-11T16:37:48Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject452-464http://dx.doi.org/10.1007/978-3-319-10160-6_40Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8646 LNCS, p. 452-464.1611-33490302-9743http://hdl.handle.net/11449/16765610.1007/978-3-319-10160-6_402-s2.0-84906860676Scopusreponame: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/openAccess2021-10-23T21:44:21Zoai:repositorio.unesp.br:11449/167656Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:28:57.314405Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Semi-supervised learning to support the exploration of association rules |
title |
Semi-supervised learning to support the exploration of association rules |
spellingShingle |
Semi-supervised learning to support the exploration of association rules De Carvalho, Veronica Oliveira [UNESP] Association Rules Post-processing Semi-supervised Learning (SSL) |
title_short |
Semi-supervised learning to support the exploration of association rules |
title_full |
Semi-supervised learning to support the exploration of association rules |
title_fullStr |
Semi-supervised learning to support the exploration of association rules |
title_full_unstemmed |
Semi-supervised learning to support the exploration of association rules |
title_sort |
Semi-supervised learning to support the exploration of association rules |
author |
De Carvalho, Veronica Oliveira [UNESP] |
author_facet |
De Carvalho, Veronica Oliveira [UNESP] De Padua, Renan Rezende, Solange Oliveira |
author_role |
author |
author2 |
De Padua, Renan 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] De Padua, Renan Rezende, Solange Oliveira |
dc.subject.por.fl_str_mv |
Association Rules Post-processing Semi-supervised Learning (SSL) |
topic |
Association Rules Post-processing Semi-supervised Learning (SSL) |
description |
In the last years, many approaches for post-processing association rules have been proposed. The automatics are simple to use, but they don't consider users' subjectivity. Unlike, the approaches that consider subjectivity need an explicit description of the users' knowledge and/or interests, requiring a considerable time from the user. Looking at the problem from another perspective, post-processing can be seen as a classification task, in which the user labels some rules as interesting [I] or not interesting [NI], for example, in order to propagate these labels to the other unlabeled rules. This work presents a framework for post-processing association rules that uses semi-supervised learning in which: (a) the user is constantly directed to the [I] patterns of the domain, minimizing his exploration effort by reducing the exploration space, since his knowledge and/or interests are iteratively propagated; (b) the users' subjectivity is considered without using any formalism, making the task simpler. © 2014 Springer International Publishing. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2018-12-11T16:37:48Z 2018-12-11T16:37:48Z |
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/10.1007/978-3-319-10160-6_40 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8646 LNCS, p. 452-464. 1611-3349 0302-9743 http://hdl.handle.net/11449/167656 10.1007/978-3-319-10160-6_40 2-s2.0-84906860676 |
url |
http://dx.doi.org/10.1007/978-3-319-10160-6_40 http://hdl.handle.net/11449/167656 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8646 LNCS, p. 452-464. 1611-3349 0302-9743 10.1007/978-3-319-10160-6_40 2-s2.0-84906860676 |
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 |
452-464 |
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_ |
1808129324454248448 |