Martínez Ballesteros, María del Mar2024-08-222024-08-222014Martínez Ballesteros, M.d.M. (2014). Discovering quantitative association rules: A novel approach based on evolutionary algorithms. AI Communications, 27 (7). https://doi.org/10.3233/AIC-130590.0921-7126https://hdl.handle.net/11441/162014This 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.application/pdf4 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Data miningEvolutionary algorithmsQuantitative association rulesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess