摘要
提出了适用于个性化推荐的强关联规则的概念,并给出一种基于矩阵的强关联规则挖掘算法强关联规则集合能够以较少数量的规则表示全部有效关联信息,便于管理和应用.给出的强关联规则挖掘算法只需对交易数据库进行一次扫描,在挖掘过程中不断删除非频繁项使矩阵规模逐渐减小,并且避免了对冗余规则的挖掘,从而提高了挖掘效率.通过对三组数据的实验表明:强关联规则集合包括的规则数量平均仅为规则总数的26.2%,有效解决了规则数量过多的问题.
The notion of strongest association rules （SAR） was proposed, a matrix-based algorithm was developed for mining SAR set. As the subset of the whole association rule set, SAR set includes much less rules with the special suitable form for personalized recommendation without information loss. With the SAR set mining algorithm~ the transaction database is only scanned for once, the matrix scale becomes smaller and smaller, so that the mining efficiency is improved. Experiments with three data sets show that the number of rules in SAR set in average is only 26.2% of the total number of whole association rules, which mitigates the explosion of association rules.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2009年第8期144-152,共9页
Systems Engineering-Theory & Practice
基金
河北省自然科学基金(F2008000117)
中国博士后基金(20060400705)
河北省科技攻关项目(07213508D)
关键词
数据挖掘
关联规则
个性化推荐
强关联规则
data mining
association rules
personalized recommendation
strongest association rules
