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 closeness 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 case study related to the analysis of genes expression data integrating multiple external genes annotations.
Cite
Text
Vernerey et al. "Threshold-Free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/261Markdown
[Vernerey et al. "Threshold-Free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/vernerey2022ijcai-threshold/) doi:10.24963/IJCAI.2022/261BibTeX
@inproceedings{vernerey2022ijcai-threshold,
title = {{Threshold-Free Pattern Mining Meets Multi-Objective Optimization: Application to Association Rules}},
author = {Vernerey, Charles and Loudni, Samir and Aribi, Noureddine and Lebbah, Yahia},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {1880-1886},
doi = {10.24963/IJCAI.2022/261},
url = {https://mlanthology.org/ijcai/2022/vernerey2022ijcai-threshold/}
}