Post-processing association rules: A network based label propagation approach
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-662-49192-8_47 http://hdl.handle.net/11449/168340 |
Resumo: | Association rules are widely used to find relations among items in a given database. However, the amount of generated rules is too large to be manually explored. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules. Recently, the user’s knowledge has been considered to post-process the rules, directing the exploration to the knowledge he considers interesting. However, sometimes the user wants to explore the rule set without adding his prior knowledge BIAS, exploring the rule set according to its features. Aiming to solve this problem, this paper presents an approach, named PARLP (Post-processing Association Rules using Label Propagation), that explores the entire rule set, suggesting rules to be classified by the user as “Interesting” or “Non-Interesting”. In this way, the user is directed to analyze the rules that have some importance on the rule set, so the user does not need to explore the entire rule set. Moreover, the user’s classification is propagated to all the rules using label propagation approaches, so the most similar rules will likely be on the same class. The results show that the PARLP succeeds to direct the exploration to a set of rules considered interesting, reducing the amount of association rules to be explored. |
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Repositório Institucional da UNESP |
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spelling |
Post-processing association rules: A network based label propagation approachAssociation rules are widely used to find relations among items in a given database. However, the amount of generated rules is too large to be manually explored. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules. Recently, the user’s knowledge has been considered to post-process the rules, directing the exploration to the knowledge he considers interesting. However, sometimes the user wants to explore the rule set without adding his prior knowledge BIAS, exploring the rule set according to its features. Aiming to solve this problem, this paper presents an approach, named PARLP (Post-processing Association Rules using Label Propagation), that explores the entire rule set, suggesting rules to be classified by the user as “Interesting” or “Non-Interesting”. In this way, the user is directed to analyze the rules that have some importance on the rule set, so the user does not need to explore the entire rule set. Moreover, the user’s classification is propagated to all the rules using label propagation approaches, so the most similar rules will likely be on the same class. The results show that the PARLP succeeds to direct the exploration to a set of rules considered interesting, reducing the amount of association rules to be explored.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Instituto de Ciências Matemáticas e de Computação USP - Universidade de São PauloInstituto de Geociências e Ciências Exatas UNESP - Univerdidade Estadual PaulistaInstituto de Geociências e Ciências Exatas UNESP - Univerdidade Estadual PaulistaFAPESP: 2014/08996-0FAPESP: PROEX-8434242/DUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)de Padua, Renande Carvalho, Veronica Oliveira [UNESP]Rezende, Solange Oliveira2018-12-11T16:40:52Z2018-12-11T16:40:52Z2016-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject580-591http://dx.doi.org/10.1007/978-3-662-49192-8_47Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9587, p. 580-591.1611-33490302-9743http://hdl.handle.net/11449/16834010.1007/978-3-662-49192-8_472-s2.0-84956598832Scopusreponame: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/openAccess2021-10-23T21:44:31Zoai:repositorio.unesp.br:11449/168340Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:04:46.746903Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Post-processing association rules: A network based label propagation approach |
title |
Post-processing association rules: A network based label propagation approach |
spellingShingle |
Post-processing association rules: A network based label propagation approach de Padua, Renan |
title_short |
Post-processing association rules: A network based label propagation approach |
title_full |
Post-processing association rules: A network based label propagation approach |
title_fullStr |
Post-processing association rules: A network based label propagation approach |
title_full_unstemmed |
Post-processing association rules: A network based label propagation approach |
title_sort |
Post-processing association rules: A network based label propagation approach |
author |
de Padua, Renan |
author_facet |
de Padua, Renan de Carvalho, Veronica Oliveira [UNESP] Rezende, Solange Oliveira |
author_role |
author |
author2 |
de Carvalho, Veronica Oliveira [UNESP] Rezende, Solange Oliveira |
author2_role |
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 de Carvalho, Veronica Oliveira [UNESP] Rezende, Solange Oliveira |
description |
Association rules are widely used to find relations among items in a given database. However, the amount of generated rules is too large to be manually explored. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules. Recently, the user’s knowledge has been considered to post-process the rules, directing the exploration to the knowledge he considers interesting. However, sometimes the user wants to explore the rule set without adding his prior knowledge BIAS, exploring the rule set according to its features. Aiming to solve this problem, this paper presents an approach, named PARLP (Post-processing Association Rules using Label Propagation), that explores the entire rule set, suggesting rules to be classified by the user as “Interesting” or “Non-Interesting”. In this way, the user is directed to analyze the rules that have some importance on the rule set, so the user does not need to explore the entire rule set. Moreover, the user’s classification is propagated to all the rules using label propagation approaches, so the most similar rules will likely be on the same class. The results show that the PARLP succeeds to direct the exploration to a set of rules considered interesting, reducing the amount of association rules to be explored. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 2018-12-11T16:40:52Z 2018-12-11T16:40:52Z |
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/10.1007/978-3-662-49192-8_47 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9587, p. 580-591. 1611-3349 0302-9743 http://hdl.handle.net/11449/168340 10.1007/978-3-662-49192-8_47 2-s2.0-84956598832 |
url |
http://dx.doi.org/10.1007/978-3-662-49192-8_47 http://hdl.handle.net/11449/168340 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9587, p. 580-591. 1611-3349 0302-9743 10.1007/978-3-662-49192-8_47 2-s2.0-84956598832 |
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 |
580-591 |
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_ |
1808128891601027072 |