关联规则挖掘算法研究  

A Study on Algorithm for Association Rules Mining

作　　者：邓悦[1] 赵井文[1] 

机构地区：[1]辽宁工程技术大学职业技术学院,阜新123000

出　　处：《办公自动化（综合月刊）》2008年第1期38-40,共3页Office Informatization

摘　　要：Apriori算法是发现频繁项目集的经典算法,但是该算法需反复扫描数据库,因此效率较低。针对Apriori算法,GBARM (Group Based Association Rules Mining)算法对事务集进行压缩,并且在统计Ck中各项集的支持频度时,逐步减小Ck的规模,从而改善算法的性能。Apriori is a classical algorithm of generationg frequent itemsets. But the algorithm need repeatedly scan the transaction database, and the performance will be dramatically affected. The GBRAM （Group_Based AssoCiation Rules Mining） algorithm is albe to gradually reduce the scale of the transaction used to scanning and gradually reduce the scale of the Ck at the same time. As a result, the performance of the algorithm is significantly improved.

关 键 词：关联规则 APRIORI 频繁项集 6BRAM 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]