Mining High Quality Association Rules Using Genetic Algorithms.
Peter P. Wakabi-Waiswa, Venansius Baryamureeba · 2011
Association rule mining problem (ARM) is a struc-tured mechanism for unearthing hidden facts in large data sets and drawing inferences on how a subset of items influences the presence of another subset. ARM is computationally very expensive because the number of rules grow exponentially as the number of items in the database increase. This exponential growth is exacer-bated further when data dimensions increase. The asso-ciation rule mining problem is even made more com-plex when the need to take the different rule quality metrics into account arises. In this paper, we propose a genetic algorithm (GA) to generate high quality as-sociation rules with five rule quality metrics. We study the performance of the algorithm and the experimental results show that the algorithm produces high quality rules in good computational times.