Abstract
We explore in this paper the application of bioinspired approaches to the association rules mining (ARM) problem for the purpose of accelerating the process of extracting the correlations between items in sizeable data instances. A new bio-inspired GPU-based model is proposed, which benefits from the massively GPU threading 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 wellknown 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 GPUbased ARM approaches when dealing with the challenging Webdocs instance.
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