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Utility-Peak Itemset Mining with Constraint Programming

Authors Chaima Hamdi , Nadjib Lazaar , Nassim Belmecheri , Djawad Bekkoucha , Said Jabbour , Lakhdar Saïs

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Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Artificial intelligence
Keywords
  • Constraint Programming
  • Pattern Mining
  • HUI Mining
  • Global Constraints
  • Declarative Data Mining

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Abstract

High-Utility Itemset Mining (HUIM) aims to discover itemsets whose utility exceeds a given threshold. While specialized algorithms achieve strong performance, they lack flexibility when additional domain constraints must be incorporated. Constraint Programming (CP) offers a declarative alternative, but requires strong propagation to remain competitive. In this paper, we propose a CP framework for utility-driven pattern mining based on a parameterized global constraint that unifies the enumeration of High-Utility Itemsets (HUIs) and a new condensed representation called Utility-Peak Itemsets (UPIs). A UPI is an itemset whose utility is greater or equal than that of all its immediate subsets and supersets, capturing locally utility-maximal patterns. We study the computational complexity of UPI mining and show that deciding whether a high-utility UPI exists, for a given utility threshold, is NP-complete. Our global constraint, PeakUtility, integrates utility computation and upper-bound pruning through propagation rules. Experiments demonstrate that our approach performs competitively with the state of the art HUIM algorithms while preserving the modelling flexibility of CP.

Cite As Get BibTex

Chaima Hamdi, Nadjib Lazaar, Nassim Belmecheri, Djawad Bekkoucha, Said Jabbour, and Lakhdar Saïs. Utility-Peak Itemset Mining with Constraint Programming. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.27

Author Details

Chaima Hamdi
  • LISN, CNRS, Paris-Saclay University, France
Nadjib Lazaar
  • LISN, CNRS, Paris-Saclay University, France
Nassim Belmecheri
  • Simula Research Laboratory, Oslo, Norway
Djawad Bekkoucha
  • LISN, CNRS, Paris-Saclay University, France
Said Jabbour
  • CRIL, CNRS - Université d’Artois, Lens, France
Lakhdar Saïs
  • CRIL, CNRS - Université d’Artois, Lens, France

Funding

This work was supported by the French National Research Agency (ANR) through the project FIDD (grant agreement ANR-24-CE23-0711), and in part by the DeMik project through the UPS Excellences Springboard program (grant SPATTR-2025-12).

Supplementary Materials

References

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