基于Apriori数据挖掘算法研究  

Research Association Rules of Data Mining Algorithm Based on Apriori

作　　者：郭秀娟[1] 张树彬[2] 岳俊华[1] 

机构地区：[1]吉林建筑工程学院计算机科学与工程学院,长春130021 [2]吉林省经济管理干部学院资源与环境管理系,长春130012

出　　处：《吉林建筑工程学院学报》2010年第3期57-60,共4页Journal of Jilin Architectural and Civil Engineering

摘　　要：关联规则是从数据集中识别出频繁出现的属性值集,然后利用这些频繁集创建描述关联关系的规则过程.在分析经典关联规则挖掘算法的基础上,讨论了经典的Apriori算法,并提出改进的Apriori关联规则算法,对算法进行了实验数据的算法性能分析及运行时间对比.结果表明,改进的算法在运行速度和挖掘性能上都较经典的Apriori算法都有显著提高.The associational rule refers to the sets of attribute-values,which frequently appeared in data set recognition,also named as frequent item-sets.using these frequent sets Description association relation rules process is set up.Based on the analysis of the classical algorithm for mining association rules,we summarize classical Apriori algorithm of association rules,put forward improved Apriori algorithm of association rules,whose performance was tested by testing learning data sets.The results demonstrate that the improved Apriori algorithm of association rules works better in both running speed and mining capability.

关 键 词：数据挖掘 关联规则 频繁项目集 APRIORI算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]