Metrics for Association Rule Clustering Assessment

dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniversidade de São Paulo (USP)
dc.creatorCarvalho, Veronica Oliveira de [UNESP]
dc.creatorSantos, Fabiano Fernandes dos
dc.creatorRezende, Solange Oliveira
dc.date2015-10-22T06:12:41Z
dc.date2015-10-22T06:12:41Z
dc.date2015-01-01
dc.date.accessioned2023-09-12T06:59:30Z
dc.date.available2023-09-12T06:59:30Z
dc.identifierhttp://link.springer.com/chapter/10.1007%2F978-3-662-46335-2_5
dc.identifierTransactions On Large-scale Data- And Knowledge- Centered Systems Xvii. Berlin: Springer-verlag Berlin, v. 8970, p. 97-127, 2015.
dc.identifier0302-9743
dc.identifierhttp://hdl.handle.net/11449/129596
dc.identifier10.1007/978-3-662-46335-2_5
dc.identifierWOS:000355814500005
dc.identifier1961581092362881
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8778934
dc.descriptionIssues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. 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. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.
dc.descriptionInstituto de Geociências e Ciências Exatas, UNESP - Universidade Estadual Paulista, Rio Claro, Brazil
dc.descriptionInstituto de Ciências Matemáticas e de Computação, USP - Universidade de São Paulo, São Carlos, Brazil
dc.descriptionInstituto de Geociências e Ciências Exatas, UNESP - Universidade Estadual Paulista, Rio Claro, Brazil
dc.format97-127
dc.languageeng
dc.publisherSpringer
dc.relationTransactions On Large-scale Data- And Knowledge- Centered Systems Xvii
dc.relation0,295
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectAssociation rules
dc.subjectPre-processing
dc.subjectClustering
dc.subjectEvaluation metrics
dc.titleMetrics for Association Rule Clustering Assessment
dc.typeTrabalho apresentado em evento


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