Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard.
Year: 2013, Volume: 14, Issue: 61, Pages: 3153−3157
In this paper, we propose
QuantMiner, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers âgoodâ 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.