Abstract
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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The first author is supported by the French National Research Agency (ANR) and Region Normandie under grant HAISCoDe.
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Bekkoucha, D., Ouali, A., Boizumault, P., Crémilleux, B. (2024). Efficiently Mining Closed Interval Patterns with Constraint Programming. In: Dilkina, B. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2024. Lecture Notes in Computer Science, vol 14742. Springer, Cham. https://doi.org/10.1007/978-3-031-60597-0_4
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