Parallel bees swarm optimization for association rules mining using GPU architecture

Abstract

This paper addresses the problem of association rules mining with large scale data sets using bees behaviors. The bees swarm optimization method have been successfully running on small and medium data size. Nevertheless, when dealing with large benchmark, it is bluntly blocked. Additionally, graphic processor units are massively threaded providing highly intensive computing and very usable by the optimization research community. The parallelization of such method on GPU architecture can be deal large data sets as the case of WebDocs in real time. In this paper, the evaluation process of the solutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×70 in most data sets, furthermore, the WebDocs benchmark is handled with less than forty hours.