Abstract:
For reducing the spaces of rule database and facilitating users to query, the minimal prediction set is used and mined using maximum frequent item sets which are found by a set-enumeration tree. The effectiveness of rule expansion is proved in theory. Experimental results show that it is efficient to reduce 1 order of the traditional one.
Key words:
association rules,
set-enumeration tree,
minimal prediction set,
maximum frequent item sets
摘要: 为缩减关联规则存储空间和方便查询关联规则,提出一种前件为单一项目的最小预测集算法。利用集合枚举树找到最大频繁项 目集,据此来挖掘最小预测集。对规则扩展的有效性进行证明。实验结果表明,通过该算法得到的最小预测集比传统方法小1个数量级。
关键词:
关联规则,
集合枚举树,
最小预测集,
最大频繁集
CLC Number:
ZHANG Jun; CHEN Kai-ming. Algorithm of Mining Minimal Prediction Set Based on Set-enumeration Tree[J]. Computer Engineering, 2008, 34(9): 76-77,8.
张 军;陈凯明. 基于集合枚举树的最小预测集挖掘算法[J]. 计算机工程, 2008, 34(9): 76-77,8.