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Conference papers

Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules

Abstract : Constraint-based pattern mining is at the core of numerous data mining tasks. Unfortunately, thresholds which are involved in these constraints cannot be easily chosen. This paper investigates a Multi-objective Optimization approach where several (often conflicting) functions need to be optimized at the same time. We introduce a new model for efficiently mining Pareto optimal patterns with constraint programming. Our model exploits condensed pattern representations to reduce the mining effort. To this end, we design a new global constraint for ensuring the closedness of patterns over a set of measures. We show how our approach can be applied to derive high-quality non redundant association rules without the use of thresholds whose added-value is studied on both UCI datasets and a case study related to the analysis of genes expression data integrating multiple external genes annotations.
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Conference papers
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Contributor : Samir Loudni Connect in order to contact the contributor
Submitted on : Friday, June 3, 2022 - 10:02:06 AM
Last modification on : Saturday, June 25, 2022 - 3:53:04 AM


  • HAL Id : hal-03687132, version 1


Charles Vernerey, Samir Loudni, Noureddine Aribi, yahia Lebbah. Threshold-free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules. IJCAI-ECAI 2022 - 31ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, Jul 2022, Messe Wien, Vienna, Austria. ⟨hal-03687132⟩



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