Efficient mining of both positive and negative association rules

被引：297
|
作者：
Wu, XD
Zhang, CQ
Zhang, SC
机构：
[1] Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA
[2] Univ Technol Sydney, Fac Informat Technol, Broadway, NSW 2007, Australia
[3] Tsinghua Univ, Beijing 100084, Peoples R China
来源：
ACM TRANSACTIONS ON INFORMATION SYSTEMS | 2004年 / 22卷 / 03期
关键词：
algorithms; experimentation; association rules; negative associations;
D O I：
10.1145/1010614.1010616
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an efficient method for mining both positive and negative association rules in databases. The method extends traditional associations to include association rules of forms A double right arrow (sic)B, (sic)A double right arrow B, and (sic)A double right arrow (sic)B, which indicate negative associations between itemsets. With a pruning strategy and an interestingness measure, our method scales to large databases. The method has been evaluated using both synthetic and real-world databases, and our experimental results demonstrate its effectiveness and efficiency.
引用
收藏
页码：381 / 405
页数：25
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