Post-processing association rules: A network based label propagation approach

Detalhes bibliográficos
Autor(a) principal: de Padua, Renan
Data de Publicação: 2016
Outros Autores: 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/10.1007/978-3-662-49192-8_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.
id UNSP_a4f64b845647609e9eb96156d44af5af
oai_identifier_str oai:repositorio.unesp.br:11449/168340
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
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