Mining multi-dimensional association rules with multiple support constraints

被引：0

作者：

Lin, WY ^{[1
]}

Tseng, MC ^{[1
]}

机构：

[1] I SHou Univ, Dept Informat Mangement, Kaohsiung 84041, Taiwan

来源：

7TH WORLD MULTICONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL II, PROCEEDINGS: COMPUTER SCIENCE AND ENGINEERING | 2003年

关键词：

data mining; multi-dimensional association rules; multiple minimum supports and taxonomy;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mining multi-dimensional association rules from a data warehouse has been recognized as an important model in data mining. Earlier work on multi-dimensional association rules confined the minimum supports to be uniformly specified for all items and/or ignored the schema hierarchy between dimensional attributes. This constraint would restrain an expert from discovering some deviations or exceptions that are more interesting but much less supported than general trends. In this paper, we extended the model of mining multi-dimensional association rules to take into account the schema hierarchy and to allow user-specified multiple minimum supports automatically. We also proposed an algorithm, MMS_MDAR, for discovering multi-dimensional frequent itemsets. The experimental result shows our approach is effective.

引用

页码：256 / 261

页数：6

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