Analyzing the subjective interestingness of association rules

被引：111

作者：

Liu, B ^{[1
]}

Hsu, W ^{[1
]}

Chen, S ^{[1
]}

Ma, YM ^{[1
]}

机构：

[1] Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore 117543, Singapore

来源：

IEEE INTELLIGENT SYSTEMS & THEIR APPLICATIONS | 2000年 / 15卷 / 05期

关键词：

We would like to thank many people; especially Minqing Hu; Ken Wong; and Yiyuan Xia; for their contributions to the project. The project is funded by the National Science and Technology Board and National University of Singapore;

D O I：

10.1109/5254.889106

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new approach to help users find interesting rules from a set of discovered association rules is described. This interestingness analysis system (IAS) leverages the user's existing domain knowledge to analyze discovered associations and then rank discovered rules according to various interestingness criteria.

引用

页码：47 / 55

页数：9

共 24 条

[1]

Adomavicius G., 1997, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, P111

[2]

Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072

[3]

Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487

[4]

[Anonymous], P ACM SIGMOD 98

[5]

[Anonymous], ADV KNOWLEDGE DISCOV

[6]

[Anonymous], P 1997 INT C KNOWL D

[7]

Bayardo R.J., 1999, P 5 ACM SIGKDD INT C, P145, DOI [10.1145/312129.312219, DOI 10.1145/312129.312219]

[8]

BOOSE J, 1993, KNOWL ACQUIS, P39

[9]

HAN J, 1996, P SIGMOD 96 WORKSH R, P27

[10]

IMIELINSKI T, 1996, P 2 INT C KNOWL DISC, P256

← 1 2 3 →