Efficiently Mining Closed Interval Patterns with Constraint Programming - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Efficiently Mining Closed Interval Patterns with Constraint Programming

Résumé

Constraint programming (CP) has become increasingly prevalent in recent years for performing pattern mining tasks, particularly on binary datasets. While numerous CP models have been designed for mining on binary data, there does not exist any model designed for mining on numerical datasets. Therefore these kinds of datasets need to be pre-processed to fit the existing methods. Afterward a post-processing is also required to recover the patterns into a numerical format. This paper presents two CP approaches for mining closed interval patterns directly from numerical data. Our proposed models seamlessly execute pattern mining tasks without any loss of information or the need for pre- or post-processing steps. Experiments conducted on different numerical datasets demonstrate the effectiveness of our proposed CP models compared to other methods.
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Dates et versions

hal-04644326 , version 1 (21-08-2024)

Identifiants

Citer

Djawad Bekkoucha, Abdelkader Ouali, Patrice Boizumault, Bruno Crémilleux. Efficiently Mining Closed Interval Patterns with Constraint Programming. The 21st International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2024), May 2024, Uppasala, Sweden. pp.51-67, ⟨10.1007/978-3-031-60597-0_4⟩. ⟨hal-04644326⟩
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