Semi-supervised learning to support the exploration of association rules

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dc.contributor.authorDe Carvalho, Veronica Oliveira [UNESP]
dc.contributor.authorDe Padua, Renan
dc.contributor.authorRezende, Solange Oliveira
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2018-12-11T16:37:48Z
dc.date.available2018-12-11T16:37:48Z
dc.date.issued2014-01-01
dc.description.abstractIn 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.en
dc.description.affiliationInstituto de Geociências e Ciências Exatas, UNESP - Univ. Estadual Paulista, Rio Claro
dc.description.affiliationInstituto de Ciências Matemáticas e de Computaçã o, USP - Universidade de São Paulo, São Carlos
dc.description.affiliationUnespInstituto de Geociências e Ciências Exatas, UNESP - Univ. Estadual Paulista, Rio Claro
dc.format.extent452-464
dc.identifierhttp://dx.doi.org/10.1007/978-3-319-10160-6_40
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8646 LNCS, p. 452-464.
dc.identifier.doi10.1007/978-3-319-10160-6_40
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-84906860676
dc.identifier.urihttp://hdl.handle.net/11449/167656
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofsjr0,295
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectAssociation Rules
dc.subjectPost-processing
dc.subjectSemi-supervised Learning (SSL)
dc.titleSemi-supervised learning to support the exploration of association rulesen
dc.typeTrabalho apresentado em evento

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