一种基于BUC的水平加权关联规则挖掘算法

关联规则挖掘可以从大量数据中发现项集间潜在而有趣的相互联系。针对用户对每个项目感兴趣的程度不同，一些学者提出了水平加权关联规则。然而每次生成新候选集后对整个数据库事物的扫描成为算法效率的一大瓶颈。为进一步提高加权关联规则的挖掘效率，在原有的水平加权关联规则算法的基础上，采取了深度优先的策略，提出了一种基于BUC的水平加权关联规则挖掘算法——BUc—MINWAL。改进算法可以大大减少对数据库的扫描范围。实验结果表明，改进的算法有更好的执行效率。

A BUC-BASED MINING ALGORITHM FOR HORIZONTAL WEIGHTED ASSOCIATION RULES

Association rules mining can find out latent and interesting rules form large volumes of data.According to the fact that customers pay different attention to different items,some scholars proposed algorithms for association rules with horizontal weighted items.But every time after forming a new candidate items group,the scan of whole transactions in database is still the bottleneck of the algorithm efficiency.In order to enhance the performance of mining,in this article we imbue the previous algorithms with...