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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/76645
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dc.contributor.authorDe Carvalho, Veronica Oliveira-
dc.contributor.authorDos Santos, Fabiano Fernandes-
dc.contributor.authorRezende, Solange Oliveira-
dc.date.accessioned2014-05-27T11:30:45Z-
dc.date.accessioned2016-10-25T18:54:28Z-
dc.date.available2014-05-27T11:30:45Z-
dc.date.available2016-10-25T18:54:28Z-
dc.date.issued2013-09-26-
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-40131-2_21-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259.-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttp://hdl.handle.net/11449/76645-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/76645-
dc.description.abstractMany topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness. © 2013 Springer-Verlag GmbH.en
dc.format.extent248-259-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectAssociation Rules-
dc.subjectClustering-
dc.subjectPre-processing-
dc.subjectAssociation mining-
dc.subjectPre-processing step-
dc.subjectResearch communities-
dc.subjectSuitable solutions-
dc.subjectData warehouses-
dc.subjectAssociation rules-
dc.titleMetrics to support the evaluation of association rule clusteringen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionUniversidade de São Paulo (USP)-
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.identifier.doi10.1007/978-3-642-40131-2_21-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.identifier.scopus2-s2.0-84884493837-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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