Novel approach to optimize quantitative association rules by employing multi-objective genetic algorithm

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dc.contributor.authorKaya, M
dc.contributor.authorAlhajj, R
dc.date.accessioned2026-08-12T17:00:39Z
dc.date.issued2005
dc.departmentFırat Üniversitesi
dc.description18th International Industrial and Engineering Applications of Artificial Intelligence and Expert Systems -- JUN 22-24, 2005 -- Bari, ITALY
dc.description.abstractThis paper proposes two novel methods to optimize quantitative association rules. We utilize a multi-objective Genetic Algorithm (GA) in the process. One of the methods deals with partial optimal, and the other method investigates complete optimal. Experimental results on Letter Recognition Database from UCI Machine Learning Repository demonstrate the effectiveness and applicability of the proposed approaches.
dc.description.sponsorshipUniv Bari, Dept Comp Sci
dc.identifier.endpage562
dc.identifier.isbn3-540-26551-1
dc.identifier.issn0302-9743
dc.identifier.orcid0000-0003-2995-8282
dc.identifier.startpage560
dc.identifier.urihttps://hdl.handle.net/11508/47302
dc.identifier.volume3533
dc.identifier.wosWOS:000230355800078
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer-Verlag Berlin
dc.relation.ispartofInnovations in Applied Artificial Intelligence
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260511
dc.titleNovel approach to optimize quantitative association rules by employing multi-objective genetic algorithm
dc.typeConference Object

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