QuantMiner for Mining Quantitative Association Rules

QuantMiner for Mining Quantitative Association Rules

Ansaf Salleb-Aouissi (1) , Christel Vrain (2) , Cyril Nortet (2) , Xiangrong Kong (1) , Vivek Rathod (1) , Daniel Cassard (3)

1 CCLS - Center for Computational Learning Systems
2 LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
3 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)

In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers "good" intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QUANTMINER as an interactive, exploratory data mining tool.

Mots clés: association rules; numerical and categorical attributes; unsupervised discretization; genetic algorithm; simulated annealing

Domaines: Apprentissage [cs.LG]

Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, et al.. QuantMiner for Mining Quantitative Association Rules. Journal of Machine Learning Research, 2013, 14, pp.3153-3157. ⟨hal-00912453⟩