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DC Field | Value | Language |
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dc.contributor.author | De Carvalho, Veronica Oliveira | - |
dc.contributor.author | Dos Santos, Fabiano Fernandes | - |
dc.contributor.author | Rezende, Solange Oliveira | - |
dc.date.accessioned | 2014-05-27T11:30:45Z | - |
dc.date.accessioned | 2016-10-25T18:54:28Z | - |
dc.date.available | 2014-05-27T11:30:45Z | - |
dc.date.available | 2016-10-25T18:54:28Z | - |
dc.date.issued | 2013-09-26 | - |
dc.identifier | http://dx.doi.org/10.1007/978-3-642-40131-2_21 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8057 LNCS, p. 248-259. | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | http://hdl.handle.net/11449/76645 | - |
dc.identifier.uri | http://acervodigital.unesp.br/handle/11449/76645 | - |
dc.description.abstract | Many 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.extent | 248-259 | - |
dc.language.iso | eng | - |
dc.source | Scopus | - |
dc.subject | Association Rules | - |
dc.subject | Clustering | - |
dc.subject | Pre-processing | - |
dc.subject | Association mining | - |
dc.subject | Pre-processing step | - |
dc.subject | Research communities | - |
dc.subject | Suitable solutions | - |
dc.subject | Data warehouses | - |
dc.subject | Association rules | - |
dc.title | Metrics to support the evaluation of association rule clustering | en |
dc.type | outro | - |
dc.contributor.institution | Universidade Estadual Paulista (UNESP) | - |
dc.contributor.institution | Universidade de São Paulo (USP) | - |
dc.description.affiliation | Instituto de Geociências e Ciências Exatas UNESP - Univ. Estadual Paulista, Rio Claro | - |
dc.description.affiliation | Instituto de Ciências Matemáticas e de Computaçã o USP - Universidade de São Paulo, São Carlos | - |
dc.description.affiliationUnesp | Instituto de Geociências e Ciências Exatas UNESP - Univ. Estadual Paulista, Rio Claro | - |
dc.identifier.doi | 10.1007/978-3-642-40131-2_21 | - |
dc.rights.accessRights | Acesso restrito | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.identifier.scopus | 2-s2.0-84884493837 | - |
Appears in Collections: | Artigos, TCCs, Teses e Dissertações da Unesp |
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