Subjective evaluation of labeling methods for association rule clustering

dc.contributor.authorDe Padua, Renan
dc.contributor.authorDos Santos, Fabiano Fernandes
dc.contributor.authorDa Silva Conrado, Merley
dc.contributor.authorDe Carvalho, Veronica Oliveira [UNESP]
dc.contributor.authorRezende, Solange Oliveira
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T18:59:24Z
dc.date.available2022-04-28T18:59:24Z
dc.date.issued2013-12-01
dc.description.abstractAmong the post-processing association rule approaches, clustering is an interesting one. When an association rule set is clustered, the user is provided with an improved presentation of the mined patters. The domain to be explored is structured aiming to join association rules with similar knowledge. To take advantage of this organization, it is essential that good labels be assigned to the groups, in order to guide the user during the association rule exploration process. Few works have explored and proposed labeling methods for this context. Moreover, these methods have not been explored through subjective evaluations in order to measure their quality; usually, only objective evaluations are used. This paper subjectively evaluates five labeling methods used on association rule clustering. The evaluation aims to find out the methods that presents the best results based on the analysis of the domain experts. The experimental results demonstrate that there is a disagreement between objective and subjective evaluations as reported in other works from literature. © Springer-Verlag 2013.en
dc.description.affiliationInstituto de Ciências Matemáticas e de Computaçã o USP - Universidade de São Paulo, São Carlos
dc.description.affiliationInstituto de Geociências e Ciências Exatas UNESP - Univ Estadual Paulista, Rio Claro
dc.description.affiliationUnespInstituto de Geociências e Ciências Exatas UNESP - Univ Estadual Paulista, Rio Claro
dc.format.extent289-300
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-45111-9_26
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8266 LNAI, n. PART 2, p. 289-300, 2013.
dc.identifier.doi10.1007/978-3-642-45111-9_26
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-84893754595
dc.identifier.urihttp://hdl.handle.net/11449/220069
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.titleSubjective evaluation of labeling methods for association rule clusteringen
dc.typeTrabalho apresentado em evento

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