关键词: 粒子群优化算法, 灰色模型, 动态关联规则, 背景值

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.

Key words: particle swarm optimization, gray model, dynamic association rule, background value