Post-processing association rules using networks and transductive learning

Detalhes bibliográficos
Autor(a) principal: Padua, Renan De
Data de Publicação: 2014
Outros Autores: Rezende, Solange Oliveira, Carvalho, Veronica Oliveira De [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ICMLA.2014.57
http://hdl.handle.net/11449/177587
Resumo: Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules of the domain. Some of these approaches use the user's knowledge to guide the exploration according to what is defined (thought) as interesting by the user. However, this definition is done before the process starts. Therefore, the user must know what may be and what may not be interesting to him/her. This work proposes a general association rule post-processing approach that extracts the user's knowledge during the post-processing phase. That way, the user does not need to have a prior knowledge in the database. For that, the proposed approach models the association rules in a network, uses its measures to suggest rules to be classified by the user and, then, propagates these classifications to the entire network using transductive learning algorithms. Therefore, this approach treats the post-processing problem as a classification task. Experiments were carried out to demonstrate that the proposed approach reduces the number of rules to be explored by the user and directs him/her to the potentially interesting rules of the domain.
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spelling Post-processing association rules using networks and transductive learningAssociation RulesLabel PropagationNetworksPost-ProcessingPruningAssociation is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules of the domain. Some of these approaches use the user's knowledge to guide the exploration according to what is defined (thought) as interesting by the user. However, this definition is done before the process starts. Therefore, the user must know what may be and what may not be interesting to him/her. This work proposes a general association rule post-processing approach that extracts the user's knowledge during the post-processing phase. That way, the user does not need to have a prior knowledge in the database. For that, the proposed approach models the association rules in a network, uses its measures to suggest rules to be classified by the user and, then, propagates these classifications to the entire network using transductive learning algorithms. Therefore, this approach treats the post-processing problem as a classification task. Experiments were carried out to demonstrate that the proposed approach reduces the number of rules to be explored by the user and directs him/her to the potentially interesting rules of the domain.Instituto de Ciencias Matematicas e de Computacao USP-Universidade de Sao PauloInstituto de Geociencias e Ciencias Exatas Unesp-Univ Estadual PaulistaInstituto de Geociencias e Ciencias Exatas Unesp-Univ Estadual PaulistaUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Padua, Renan DeRezende, Solange OliveiraCarvalho, Veronica Oliveira De [UNESP]2018-12-11T17:26:13Z2018-12-11T17:26:13Z2014-02-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject318-323http://dx.doi.org/10.1109/ICMLA.2014.57Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, p. 318-323.http://hdl.handle.net/11449/17758710.1109/ICMLA.2014.572-s2.0-84946690635Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014info:eu-repo/semantics/openAccess2021-10-23T21:44:37Zoai:repositorio.unesp.br:11449/177587Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:10:57.127652Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Post-processing association rules using networks and transductive learning
title Post-processing association rules using networks and transductive learning
spellingShingle Post-processing association rules using networks and transductive learning
Padua, Renan De
Association Rules
Label Propagation
Networks
Post-Processing
Pruning
title_short Post-processing association rules using networks and transductive learning
title_full Post-processing association rules using networks and transductive learning
title_fullStr Post-processing association rules using networks and transductive learning
title_full_unstemmed Post-processing association rules using networks and transductive learning
title_sort Post-processing association rules using networks and transductive learning
author Padua, Renan De
author_facet Padua, Renan De
Rezende, Solange Oliveira
Carvalho, Veronica Oliveira De [UNESP]
author_role author
author2 Rezende, Solange Oliveira
Carvalho, Veronica Oliveira De [UNESP]
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 Padua, Renan De
Rezende, Solange Oliveira
Carvalho, Veronica Oliveira De [UNESP]
dc.subject.por.fl_str_mv Association Rules
Label Propagation
Networks
Post-Processing
Pruning
topic Association Rules
Label Propagation
Networks
Post-Processing
Pruning
description Association is widely used to find relations among items in a given database. However, finding the interesting patterns is a challenging task due to the large number of rules that are generated. Traditionally, this task is done by post-processing approaches that explore and direct the user to the interesting rules of the domain. Some of these approaches use the user's knowledge to guide the exploration according to what is defined (thought) as interesting by the user. However, this definition is done before the process starts. Therefore, the user must know what may be and what may not be interesting to him/her. This work proposes a general association rule post-processing approach that extracts the user's knowledge during the post-processing phase. That way, the user does not need to have a prior knowledge in the database. For that, the proposed approach models the association rules in a network, uses its measures to suggest rules to be classified by the user and, then, propagates these classifications to the entire network using transductive learning algorithms. Therefore, this approach treats the post-processing problem as a classification task. Experiments were carried out to demonstrate that the proposed approach reduces the number of rules to be explored by the user and directs him/her to the potentially interesting rules of the domain.
publishDate 2014
dc.date.none.fl_str_mv 2014-02-05
2018-12-11T17:26:13Z
2018-12-11T17:26:13Z
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/10.1109/ICMLA.2014.57
Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, p. 318-323.
http://hdl.handle.net/11449/177587
10.1109/ICMLA.2014.57
2-s2.0-84946690635
url http://dx.doi.org/10.1109/ICMLA.2014.57
http://hdl.handle.net/11449/177587
identifier_str_mv Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, p. 318-323.
10.1109/ICMLA.2014.57
2-s2.0-84946690635
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 318-323
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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