计算机工程与应用2018，Vol.54Issue(8)：160-165,6.DOI:10.3778/j.issn.1002-8331.1611-0067
改进的PSOGM算法在动态关联规则挖掘中的应用
Application of improved PSOGM algorithm in dynamic association rule mining
摘要
Abstract
According to the analysis and prediction of the trend of support vector and confidence vector in the mining of dynamic association rules, an improved Grey Model of Particle Swarm Optimization(PSOGM)with buffer operator is proposed. Due to the introduction of the background value, which leads to the declining accuracy of the gray model prediction as well as some limitations,it is necessary to introduce the parameters to modify,by using the improved particle swarm optimization algorithm which joins two search mechanism to improve the local search ability of the algorithm,and background values of the gray model are optimized at different times, then the prediction accuracy of grey model is improved. Through the experimental simulation on the Matlab platform with the data set, the results manifest that this method is more accurate than the original grey model, grey model with genetic algorithm and the standard grey model with the particle swarm optimization.关键词
粒子群优化算法/灰色模型/动态关联规则/背景值Key words
particle swarm optimization/gray model/dynamic association rule/background value分类
信息技术与安全科学引用本文复制引用
郭世伟,孟昱煜,陈绍立..改进的PSOGM算法在动态关联规则挖掘中的应用[J].计算机工程与应用,2018,54(8):160-165,6.基金项目
甘肃省自然科学基金(No.1606RJZA033). （No.1606RJZA033）
