Constraint-based rule mining in large, dense databases

被引：126

作者：

Bayardo, RJ ^{[1
]}

Agrawal, R ^{[1
]}

Gunopulos, D ^{[1
]}

机构：

[1] IBM Corp, Almaden Res Ctr, San Jose, CA 95120 USA

来源：

DATA MINING AND KNOWLEDGE DISCOVERY | 2000年 / 4卷 / 2-3期

关键词：

data mining; association rules; rule induction;

D O I：

10.1023/A:1009895914772

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Constraint-based rule miners find all rules in a given data-set meeting user-specified constraints such as minimum support and confidence. We describe a new algorithm that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational tables). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of "frequent itemsets".

引用

页码：217 / 240

页数：24

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