Taşer, Pelin YıldırımBirant, Kökten UlaşBirant, Derya2022-02-152022-02-1520201300-06321300-0632https://doi.org/10.3906/elk-1905-88https://app.trdizin.gov.tr/makale/TXpNMU1EazNOdz09https://hdl.handle.net/20.500.14034/441Recently, 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.eninfo:eu-repo/semantics/openAccessBilgisayar BilimleriYapay ZekaBilgisayar BilimleriSibernitikBilgisayar BilimleriDonanım ve MimariBilgisayar BilimleriBilgi SistemleriBilgisayar BilimleriYazılım MühendisliğiBilgisayar BilimleriTeori ve MetotlarMühendislikElektrik ve ElektronikAssociation rule miningmultitask learningdata miningthe frequent pattern (FP)-Growth algorithmParallelAlgorithmsClassificationArticle10.3906/elk-1905-88282933955Q4WOS:0005224478000242-s2.0-85085018559TXpNMU1EazNOdz09Q3