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Authors: Jerry Lonlac 1 ; Imen Dlala 2 ; Said Jabbour 3 ; Engelbert Nguifo 4 ; Badran Raddaoui 5 and Lakhdar Sais 3

Affiliations: 1 Centre de Recherche, IMT Lille Douai, Université Lille, Lens, France ; 2 Pôle Universitaire Léonard de Vinci, Research Center, Paris La Défense, France ; 3 CRIL, University of Artois & CNRS, Lens, France ; 4 Univ. Clermont Auvergne, CNRS, LIMOS, F-63000 Clermont-Ferrand, France ; 5 SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, France

Keyword(s): Data Mining, Gradual Patterns, Propositional Satisfiability.

Abstract: This paper proposes a constraint-based modeling approach for mining frequent gradual patterns from numerical data. Our declarative approach provides a principle way to take advantage of recent advancements in satisfiability testing and several features of modern SAT solvers to enumerating gradual patterns. Interestingly, our approach can easily be extended with extra requirements, such as temporal constraints used to extract more specific patterns in a broad range of gradual patterns mining applications. An empirical evaluation on two real-word datasets shows the efficiency of our approach.

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Paper citation in several formats:
Lonlac, J.; Dlala, I.; Jabbour, S.; Nguifo, E.; Raddaoui, B. and Sais, L. (2023). Extracting Frequent Gradual Patterns Based on SAT. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-664-4; ISSN 2184-285X, SciTePress, pages 136-143. DOI: 10.5220/0012126000003541

@conference{data23,
author={Jerry Lonlac. and Imen Dlala. and Said Jabbour. and Engelbert Nguifo. and Badran Raddaoui. and Lakhdar Sais.},
title={Extracting Frequent Gradual Patterns Based on SAT},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA},
year={2023},
pages={136-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012126000003541},
isbn={978-989-758-664-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - DATA
TI - Extracting Frequent Gradual Patterns Based on SAT
SN - 978-989-758-664-4
IS - 2184-285X
AU - Lonlac, J.
AU - Dlala, I.
AU - Jabbour, S.
AU - Nguifo, E.
AU - Raddaoui, B.
AU - Sais, L.
PY - 2023
SP - 136
EP - 143
DO - 10.5220/0012126000003541
PB - SciTePress

- Science and Technology Publications, Lda.
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