How to exploit high performance computing in population-based metaheuristics for solving association rule mining problem

The application of population-based metaheuristics approaches to the association rules mining problem is explored in this paper. The combination of GPU and cluster-based parallel computing techniques is investigated for the purpose of accelerating the process of extracting the correlations between items in sizeable data instances. We propose four parallel-based approaches that benefit from the cluster intensive computing in the generation process and the massively GPU threading. This is by evaluating the association rules in parallel on GPU. To validate the proposed approaches, the most used population-based metaheuristics (GA, PSO, and BSO) have been executed on a cluster of GPUs to solve benchmarks of large and big ARM instances. We used Intel Xeon 64bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that the BSO outperforms GA and PSO. They also show that the proposed solution outperforms the HPC-based ARM approaches when exploring Webdocs instance (the largest instance existing on the web). To our knowledge, this is the first work that explores the combination of GPU and cluster-based parallel computing with the population-based metaheuristics in association rule mining.