基于兴趣度剪枝的Apriori优化算法  

Optimized Apriori algorithm based on interestingness measure pruning

作　　者：刘上力[1] 杨清[1] 

机构地区：[1]湖南科技大学网络信息中心,湖南湘潭411201

出　　处：《郑州轻工业学院学报（自然科学版）》2011年第4期68-71,共4页Journal of Zhengzhou University of Light Industry:Natural Science

基　　金：湖南省教育厅重点科学研究项目(10A028);湖南省科技计划项目(JT3031)

摘　　要：鉴于关联规则挖掘中的Apriori算法在挖掘潜在有价值、低支持度模式时效率较低,因此提出一种优化的Apriori挖掘算法,即在频繁项集挖掘中引入项项正相关兴趣度量剪枝策略,有效过滤掉非正相关长模式和无效项集,从而扩大了可挖掘支持度阈值范围.实验结果表明,该算法是有效和可行的.To solve the problem that the Apriori algorithm of mining association rules in database mining is not quite effective in the process of mining potentially valuable low-support patterns,an optimized apriori mining algorithm was proposed.This algorithm exploits an efficient pruning strategy which uses the interestingness measure to filter the non-positive correlated long model and invalid itemsets.The range of support threshold is expanded.The experimental results indicated that the given algorithm was efficient and feasible.

关 键 词：APRIORI算法 频繁项集 兴趣度 项项正相关 剪枝 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]