应用于长频繁集挖掘的基于变动邻域搜索的遗传算法设计

提出了一种基于变动邻域搜索的长频繁集挖掘方法(VNS-GA),利用遗传算法的高效搜索性能快速挖掘最大频繁集。在遗传算法的适应度函数设计中,综合考虑项集支持度、长度以及项集支持度和邻域中心支持度的距离,算法一次运行可找出邻域内的最大频繁集,改变邻域中心即可找到我们需要的最大频繁集。算法有效性通过实验得到了验证,且实验表明该算法的时间复杂度与支持度阈值大小无关,因此对于长模式挖掘问题具有很高的效率。

Genetic Algorithms Based on Variable Neighborhood Search for Mining Long Frequent Itemsets

This paper proposes a method for mining long frequent items based on variable neighborhood search(VNS-GA).Using the high searching efficiency of GA,the maximum frequent patterns can be mined rapidly.In designing the fitness function,we consider at the same time the support of itemset,length and the distance between itemset's support and the central support of neighborhood.Running the algorithm once,the maximum frequent itemsets within the neighborhood can be found,and by changing the central support of neightborhood,we can find the maximum frequent itemsets we are interested in.The validity of this method has been tested by experiments.It has been demonstrated that the VNS-GA algorithm has high efficiency in long pattern mining problem because its time complexity is free of support threshold.