Mining association rules in incomplete information systems

被引：4
|
作者：
Luo Ke [1 ]
Wang Li-li [1 ,2 ]
Tong Xiao-jiao [1 ]
机构：
[1] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Changsha 410076, Hunan, Peoples R China
[2] Dezhou Univ, Dept Comp Sci & Technol, Dezhou 253023, Peoples R China
来源：
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY | 2008年 / 15卷 / 05期
基金：
中国国家自然科学基金;
关键词：
association rules; rough sets; prediction support; prediction confidence; incomplete information system;
D O I：
10.1007/s11771-008-0135-3
中图分类号：
TF [冶金工业];
学科分类号：
0806 ;
摘要：
Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.
引用
收藏
页码：733 / 737
页数：5
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