Pelin Yıldırım TAŞER, Kökten Ulaş BİRANT, Derya BİRANT

Multitask-based association rule mining

Recently, 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 investigation and, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novel algorithm, named multitask association rule miner MTARM , that tends to jointly discover rules by considering multiple tasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of the proposed approach, highly frequent local rules single-task rules are explored for each task separately and then these local rules are combined to produce the global result multitask rules using a majority voting mechanism. Experiments were conducted on four different real-world multitask learning datasets. The experimental results indicated that the proposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly considering the relationships among multiple tasks.

Keywords:

Association rule mining multitask learning, data mining, the frequent pattern FP -Growth algorithm,

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Turkish Journal of Electrical Engineering and Computer Science-Cover

ISSN: 1300-0632
Yayın Aralığı: Yılda 6 Sayı
Yayıncı: TÜBİTAK

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