Discovery of association rules in tabular data

被引：8
|
作者：
Richards, G [1 ]
Rayward-Smith, VJ [1 ]
机构：
[1] Univ E Anglia, Sch Informat Syst, Norwich NR4 7TJ, Norfolk, England
来源：
2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2001年
关键词：
D O I：
10.1109/ICDM.2001.989553
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we address the problem of finding all association rules in tabular data. An Algorithm, ARA, for finding rules, that satisfy clearly specified constraints, in tabular data is presented. ARA is based on the Dense Miner algorithm but includes an additional constraint and an improved method of calculating support. ARA is tested and compared with our implementation of Dense Miner, it is concluded that ARA is usually more efficient than Dense Miner and is often considerably more so. We also consider the potential for modifying the constraints used in ARA in order to find more general rules.
引用
收藏
页码：465 / 472
页数：8
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