PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets  

作　　者：JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei 

机构地区：[1]Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,Department of Computer Science and Technology,Jiangnan University,Wuxi 214122,China [2]China Ship Scientific Research Center,Wuxi 214082,China [3]Taihu Laboratory of Deepsea Technological Science,Wuxi 214082,China [4]Department of Mathematics,Nanjing University,Nanjing 210023,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第4期965-975,共11页系统工程与电子技术（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(62073155,62002137,62106088,62206113);the High-End Foreign Expert Recruitment Plan(G2023144007L);the Fundamental Research Funds for the Central Universities(JUSRP221028).

摘　　要：Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search space.EAs have good running and mining performance,but they still require huge computational resource and may miss many HUIs.Due to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded processors.Experiments show that the mining performance of PHUI-GA outperforms the existing EAs.When mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关 键 词：high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance 

分 类 号：TP332[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]