Multitask-based association rule mining

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dc.authoridYILDIRIM, Pelin/O-6422-2019
dc.authoridBirant, Derya/U-6211-2017
dc.contributor.authorTaşer, Pelin Yıldırım
dc.contributor.authorBirant, Kökten Ulaş
dc.contributor.authorBirant, Derya
dc.date.accessioned2022-02-15T16:58:38Z
dc.date.available2022-02-15T16:58:38Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.description.abstractRecently, there has been a growing interest in association rule mining (ARM) in various fields. However,standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigationand, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novelalgorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multipletasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of theproposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then theselocal rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experimentswere conducted on four different real-world multitask learning datasets. The experimental results indicated that theproposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly consideringthe relationships among multiple tasks.en_US
dc.identifier.doi10.3906/elk-1905-88
dc.identifier.endpage955en_US
dc.identifier.issn1300-0632
dc.identifier.issn1300-0632
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85085018559en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage933en_US
dc.identifier.trdizinidTXpNMU1EazNOdz09en_US
dc.identifier.urihttps://doi.org/10.3906/elk-1905-88
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TXpNMU1EazNOdz09
dc.identifier.urihttps://hdl.handle.net/20.500.14034/441
dc.identifier.volume28en_US
dc.identifier.wosWOS:000522447800024en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYapay Zekaen_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectSibernitiken_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectDonanım ve Mimarien_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectBilgi Sistemlerien_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectYazılım Mühendisliğien_US
dc.subjectBilgisayar Bilimlerien_US
dc.subjectTeori ve Metotlaren_US
dc.subjectMühendisliken_US
dc.subjectElektrik ve Elektroniken_US
dc.subjectAssociation rule miningen_US
dc.subjectmultitask learningen_US
dc.subjectdata miningen_US
dc.subjectthe frequent pattern (FP)-Growth algorithmen_US
dc.subjectParallelen_US
dc.subjectAlgorithmsen_US
dc.subjectClassificationen_US
dc.titleMultitask-based association rule miningen_US
dc.typeArticleen_US

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