Association rule mining is a well-known methodology to discover significant and apparently hidden relations among
attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find interesting association
rules. However, the rule-matching task of such techniques usually requires high computational and memory requirements. The use
of efficient computational techniques has become a task of the utmost importance due to the high volume of generated data
nowadays. Hence, this paper aims at improving the scalability of quantitative association rule mining techniques based on
genetic algorithms to handle large-scale datasets without quality loss in the results obtained. For this purpose, a new
representation of the individuals, new genetic operators and a windowing-based learning scheme are proposed to achieve
successfully such challenging task. Specifically, the proposed techniques are integrated into the multi-objective evolutionary
algorithm named QARGA-M to assess their performances. Both the standard version and the enhanced one of QARGA-M have
been tested in several datasets that present different number of attributes and instances. Furthermore, the proposed methodologies
have been integrated into other existing techniques based in genetic algorithms to discover quantitative association rules. The
comparative analysis performed shows significant improvements of QARGA-M and other existing genetic algorithms in terms of
computational costs without losing quality in the results when the proposed techniques are applied.Ministerio de Ciencia y Tecnología TIN2011- 28956-C02-02Junta de Andalucía TIC-7528Junta de Andalucía P12-TIC-1728Universidad Pablo de Olavide APPB81309
