Multitask-based association rule mining

Pelin Yildirim Taşer,Kökten Ulaş Bi̇rant,Derya Bi̇rant

doi:10.3906/elk-1905-88

Pelin Yildirim Taşer, Kökten Ulaş Bi̇rant + Show 1 more

Open Access

https://doi.org/10.3906/elk-1905-88

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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.

Highlights

Association rule mining (ARM) has been extensively used to extract hidden and interesting rules from a large collection of data [1]
This paper proposes a novel algorithm, named multitask association rule miner (MTARM), that tends to discover rules by jointly considering multiple tasks
This paper introduces the concepts of single- and multitask rules and demonstrates the efficient implementation of the MTARM approach to discover patterns in the datasets

Summary

Introduction

Association rule mining (ARM) has been extensively used to extract hidden and interesting rules from a large collection of data [1] It has been an active research area owing to the challenges it presents and to its wide applications in various fields such as market basket analysis [2], recommendation systems [3], anomaly detection [4], bioinformatics [5], and text mining [6]. It would seem that jointly discovering association rules from these related tasks would help us uncover common knowledge and improve generalization performance. This perspective is supported by empirical evidence provided by recent developments in multitask learning [7]

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Results

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