GPU-based Bio-inspired Model for Solving Association Rules Mining Problem
problem with the purpose of extracting the correlations between items in sizeable data instances. According to the state of the art, the bio-inspired approaches proved their usefulness by finding high number of satisfied rules in a reasonable time when dealing with medium size instances. These approaches are unsuitable for large databases and especially for those existing on the web such as the Webdocs instance. Recently, the Graphics Processor Units (GPU) is considered as one of the most used parallel hardware to solve large scientific complex problems. In this paper, we propose a new GPU-based model of the bio-inspired approaches for solving association rules mining problem. Our model benefits from the massively GPU threaded by evaluating multiple rules in parallel on GPU. To validate the proposed model, the most used bio-inspired approaches (GA, PSO, and BSO) have been executed on GPU to solve well-known large ARM instances. Real experiments have been carried out on an Intel Xeon 64 bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that the genetic algorithm outperforms PSO and BSO. Moreover, it outperforms the state-of-the-art GPU-based ARM approaches when dealing with the challenging Webdocs instance.
Bio-Inspried Approaches, Association rules, Parallel Algorithms, GPU Computing
ISRN :CERIST- DTISI—16-000000023--DZ