Multitask-based association rule mining

Taser P. Y., Birant K. U., Birant D.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.28, no.2, pp.933-955, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.3906/elk-1905-88
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.933-955
  • Keywords: Association rule mining, multitask learning, data mining, the frequent pattern (FP)-Growth algorithm, PARALLEL, ALGORITHMS, CLASSIFICATION
  • Dokuz Eylül University Affiliated: Yes


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.