dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorDe Carvalho, Veronica Oliveira
dc.creatorDos Santos, Fabiano Fernandes
dc.creatorRezende, Solange Oliveira
dc.creatorDe Padua, Renan
dc.date2014-05-27T11:26:50Z
dc.date2016-10-25T18:37:21Z
dc.date2014-05-27T11:26:50Z
dc.date2016-10-25T18:37:21Z
dc.date2012-06-05
dc.date.accessioned2017-04-06T01:59:04Z
dc.date.available2017-04-06T01:59:04Z
dc.identifierLecture Notes in Business Information Processing, v. 102 LNBIP, p. 66-80.
dc.identifier1865-1348
dc.identifierhttp://hdl.handle.net/11449/73368
dc.identifierhttp://acervodigital.unesp.br/handle/11449/73368
dc.identifier10.1007/978-3-642-29958-2_5
dc.identifier2-s2.0-84861668130
dc.identifierhttp://dx.doi.org/10.1007/978-3-642-29958-2_5
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/894177
dc.descriptionThe post-processing of association rules is a difficult task, since a huge number of rules that are generated are of no interest to the user. To overcome this problem many approaches have been developed, such as objective measures and clustering. However, objective measures don't reduce nor organize the collection of rules, therefore making the understanding of the domain difficult. On the other hand, clustering doesn't reduce the exploration space nor direct the user to find interesting knowledge, therefore making the search for relevant knowledge not so easy. In this context this paper presents the PAR-COM methodology that, by combining clustering and objective measures, reduces the association rule exploration space directing the user to what is potentially interesting. An experimental study demonstrates the potential of PAR-COM to minimize the user's effort during the post-processing process. © 2012 Springer-Verlag.
dc.languageeng
dc.relationLecture Notes in Business Information Processing
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAssociation rules
dc.subjectClustering
dc.subjectObjective measures
dc.subjectPost-processing
dc.subjectExperimental studies
dc.subjectObjective measure
dc.subjectPost processing
dc.subjectInformation systems
dc.titlePAR-COM: A new methodology for post-processing association rules
dc.typeOtro


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