Mining association rules in incomplete information systems  

作　　者：罗可 王丽丽 童小娇 

机构地区：[1]School of Computer and Communication Engineering, Changsha University of Science and Technology,Changsha 410076, China [2]School of Computer and Communication Engineering, Changsha University of Science and Technology [3]Department of Computer Science and Technology, Dezhou University, Dezhou 253023, China

出　　处：《Journal of Central South University of Technology》2008年第5期733-737,共5页中南工业大学学报（英文版）

基　　金：Projects(10871031, 60474070) supported by the National Natural Science Foundation of China;Project(07A001) supported by the Scientific Research Fund of Hunan Provincial Education Department, China

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.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.

关 键 词：association rules rough sets prediction support prediction confidence incomplete information system 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O159[自动化与计算机技术—控制科学与工程]