Discovering quantitative association rules: A novel approach based on evolutionary algorithms

This work proposes a novel methodology to improve the discovery of quantitative association rules in continuous datasets. This methodology comprises several evolutionary algorithms able to deal with real-valued variables without performing a static discretization process. Additionally, several quality measures are analysed to select the set of measures to be optimized with the aim of finding high-quality rules.