Knowledge discovery in deductive databases with large deduction results: The first step

被引：5
|
作者：
Goh, CL
Tsukamoto, M
Nishio, S
机构：
[1] Department of Information Systems Engineering, Faculty of Engineering, Osaka University, Suita, Osaka 565
来源：
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 1996年 / 8卷 / 06期
关键词：
attribute-oriented algorithm; characteristic rule; data mining; deductive database; recursive rule;
D O I：
10.1109/69.553162
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Deductive databases have the ability to deduce new facts from a set of facts using a set of rules. They are also useful in the integration of artificial intelligence and database. However, when recursive rules are involved, the amount of deduced facts can become too large to be practically stored, viewed or analyzed. This seriously hinders the usefulness of deductive databases. In order to overcome this problem, we propose four methods to discover characteristic rules from large amount of deduction results without actually having to store all the deduction results. This paper presents the first step in the application of knowledge discovery techniques to deductive databases with large deduction results.
引用
收藏
页码：952 / 956
页数：5
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