Multitask-based association rule mining

Authors

PELİN YILDIRIM TAŞER
KÖKTEN ULAŞ BİRANT
DERYA BİRANT

DOI

10.3906/elk-1905-88

Abstract

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

First Page

933

Last Page

955

Recommended Citation

TAŞER, PELİN YILDIRIM; BİRANT, KÖKTEN ULAŞ; and BİRANT, DERYA (2020) "Multitask-based association rule mining," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 28: No. 2, Article 24. https://doi.org/10.3906/elk-1905-88
Available at: https://journals.tubitak.gov.tr/elektrik/vol28/iss2/24