摘要
在分析和介绍了分布式关联规则挖掘方法和对等网模型Kademlia的基础上,通过改进经典的Apriori算法,设计了一种能够用于对等网模型Kademlia的分布式关联规则挖掘算法。该算法通过对其频繁项集阈值的设置,能够快速减少各结点在进行关联规则挖掘时产生的中间候选项集的数量,降低算法复杂度,提高算法执行效率,仿真实验结果表明了该算法的有效性和可扩展性。
Based on analyzing both distributed association rules mining and P2P model Kademlia,by improved the classical Apriori algorithm, a distributed association rules mining is designed to implement in a model Kademlia.The algorithm use the two threshold to judge how the quickly reduceswhen the number of candidate itemsets exists.The complexity of the algorithm is reduced and the efficiency of algorithm implementation is improved.Finally,simulation results show that the performance of the improved Apriori algorithm is available and extendable.
出处
《计算机工程与设计》
CSCD
北大核心
2011年第1期221-223,323,共4页
Computer Engineering and Design
基金
广西教育厅科研基金项目(200911MS83)
