PBAR: Parallelized Brain Storm Optimization for Association Rule Mining
Lianbo Ma, Tao Zhang, Rui Wang, Guangming Yang, Yichuan Zhang · 2019
The brain storm optimization (BSO) algorithm is a new and promising swarm intelligence paradigm, based on the emerging intelligence of the human brain storming process. However, BSO is ineffective to deal with the large data sets because its clustering and idea updating operations are computationally expensive. Aiming at this issue, we propose a parallelized brain storm optimizer based on Spark framework called PLBSO. The basic idea is to parallelize the complex operations of population clustering and idea updating in BSO so as to reduce the computation cost. Especially, the generation process of new ideas is modified to make BSO more suitable for parallelism. In addition, a new parallelized algorithm called PBAR based on PLBSO is proposed for association rule mining. The performance of PLBSO is evaluated over serialized BSO on complex multimodal benchmarks. Results show that, compared with serialized BSO, PLBSO can acquire a 350% speedup approximately while keeping similar accuracy. Finally, PBAR is adopted to resolve the association rule mining problem on a transactional dataset taken from IBM SPSS modeler. The encouraging results prove the validity of our algorithms.