An Efficient Measure for Evaluating Association Rules

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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorGheraibai, Youcef
dc.contributor.authorMehdi, Malika
dc.contributor.authorBendjoudi, Ahcène
dc.date.accessioned2014-06-22T17:35:33Z
dc.date.available2014-06-22T17:35:33Z
dc.date.issued2014-08
dc.description.abstractAssociation rules mining (ARM) has attracted a lot of attention in the last decade. It aims to extract a set of relevant rules from a given database. In order to evaluate the quality of the resulting rules, existing measures, such as support and confidence, allow to evaluate the resulted rules of ARM process separately, missing the different dependencies between the rules. This paper addresses the problem of evaluating rules by taking into account two aspects: (1) The accuracy of the returned rules on the input data and (2) the distance between the returned rules. The rules set that covers the maximum of rules space is considered. To analyze the behavior of the proposed measure, it has been tested on two recent ARM algorithms BSO-ARM and HBSO-TS.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/674
dc.relation.ispartofInternation Conference on Soft Computing and Pattern Recongnitionfr_FR
dc.relation.placeTunisiafr_FR
dc.structureCalcul Pervasif et Mobilefr_FR
dc.subjectAssociation rules miningfr_FR
dc.subjectRules Qualityfr_FR
dc.subjectEvaluation of Rulesfr_FR
dc.titleAn Efficient Measure for Evaluating Association Rulesfr_FR
dc.typeConference paper
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