Title: Mining association rules for classification using frequent generator itemsets in arules package

Authors: Makhlouf Ledmi; Mohammed El Habib Souidi; Michael Hahsler; Abdeldjalil Ledmi; Chafia Kara-Mohamed

Addresses: Department of Computer Sciences – ICOSI Lab, Abbas Laghrour University of Khenchela, Khenchela, 40000, Algeria ' Department of Computer Sciences – ICOSI Lab, Abbas Laghrour University of Khenchela, Khenchela, 40000, Algeria ' Department of Computer Science, Bobby B. Lyle School of Engineering, SMU, P.O. Box 750122, Dallas, TX 75275, USA ' Department of Computer Sciences – ICOSI Lab, Abbas Laghrour University of Khenchela, Khenchela, 40000, Algeria ' Department of Computer Science – LRSD Lab, Ferhat Abbas University of Setif 1, 19000, Setif, Algeria

Abstract: Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.

Keywords: frequent generator itemsets; FGIs; classification; association rules; data mining; R language.

DOI: 10.1504/IJDMMM.2023.131399

International Journal of Data Mining, Modelling and Management, 2023 Vol.15 No.2, pp.203 - 221

Received: 05 Jun 2022
Accepted: 12 Aug 2022
Published online: 09 Jun 2023 *

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