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

Makhlouf Ledmi, Mohammed El Habib Souidi, Michael Hahsler, Abdeldjalil Ledmi and Chafia Kara-Mohamed

International Journal of Data Mining, Modelling and Management, 2023, vol. 15, issue 2, 203-221

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. (search for similar items in EconPapers)
Date: 2023
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