协同进化算法在关联规则挖掘中的应用

文中采用了一种协同进化算法，分别利用改进的遗传算法和粒子群算法对两个种群同时进行迭代，并在种群之间引入一种信息交互机制，使两个种群协同进化。文中最后通过实验对该协同进化算法、传统的遗传算法以及粒子群算法应用于关联规则挖掘时的性能进行比较，证明了该协同进化算法在可接受的时间复杂度前提下，不仅继承了传统遗传算法挖掘关联规则时无须产生规模庞大的候选项集和有效减少扫描数据库次数的优点，更弥补了其容易早熟收敛的缺陷，从而能高效地搜索出数据库中高质量的关联规则，这点在其应用于高维数据集时尤为显著。

Applied Research on Association Rules Mining with Co-evolution Algorithm

It adopts a co-evolution algorithm, which utilizes improved genetic algorithm and particle swarm optimization algorithm to it erate two populations simultaneously. Meanwhile, the mechanism of information interaction between thesetwo populations is introduced. Finally, experiments and application have been made to prove that on the premise of acceptable time complexity, not only does the co- evolution algorithm inherit the superiority of traditional genetic algorithm such as reducing the number of scanning the database effectively and generating small-scale candidate item sets, but also avoid the phenomenon of premature through comparing the properties of co-evo- lution algorithm, traditional genetic algorithm and particle swarm optimization algorithm when used in association rules mining. High quality association rules can be found when adopted the co-evolution algorithm, especially in high-dimension database.