Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
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.
Document type :
Conference papers
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03687132
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

Identifiers

  • HAL Id : hal-03687132, version 1

Citation

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⟩

Share

Metrics

Record views

0