Influence and conditional influence - New interestingness measures in association rule mining

被引：0
|
作者：
Chen, GQ [1 ]
Liu, D [1 ]
Li, JX [1 ]
机构：
[1] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China
来源：
10TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3: MEETING THE GRAND CHALLENGE: MACHINES THAT SERVE PEOPLE | 2001年
关键词：
association rule; interestingness; influence; conditional influence;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper discusses the issues of interestingness in association rule mining. First, a rule is possibly redundant or misleading even if it possesses high degrees of confidence and support. Second, association rules do not reflect the effect of negatively influential facts. Such problems are related to confidence deviation. In this paper, therefore, two new measures of interestingness, namely influence and conditional influence, are introduced to represent the effect of the antecedent on the consequent. Furthermore, the mining algorithms are extended accordingly such that certain redundant rules can he eliminated and negatively influential rules may be discovered.
引用
收藏
页码：1440 / 1443
页数：4
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