Title: A constraint programming approach for quantitative frequent pattern mining

Authors: Mohammed El Amine Laghzaoui; Yahia Lebbah

Addresses: Laboratory LITIO, Université Oran1 Ahmed Ben Bella, 31000 Oran, Algeria ' Laboratory LITIO, Université Oran1 Ahmed Ben Bella, 31000 Oran, Algeria

Abstract: Itemset mining is the first pattern mining problem studied in the literature. Most of the itemset mining studies have considered only Boolean datasets, where each transaction can contain or not items. In practical applications, items appear in some transactions with some quantities. In this paper, we propose an extension of the current efficient constraint programming approach for itemset mining, to take into account quantitative items in order to find patterns with their quantities directly on the original quantitative dataset. The contribution is two folds. Firstly, we facilitate the modelling task of mining problems through a new constraint. Secondly, we propose a new filtering algorithm to handle the frequency and closeness constraints. Experiments performed on standard benchmark datasets with numerous mining constraints show that our approach enables to find more informative quantitative patterns, which are better in running time than quantitative approaches based on classical Boolean patterns.

Keywords: itemset mining; quantitative database; closed itemset mining; constraint programming.

DOI: 10.1504/IJDMMM.2023.132973

International Journal of Data Mining, Modelling and Management, 2023 Vol.15 No.3, pp.297 - 311

Received: 07 Oct 2021
Received in revised form: 22 Sep 2022
Accepted: 27 Oct 2022
Published online: 22 Aug 2023 *

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