【作者】 金义富； 朱庆生；

【机构】 湛江师范学院计算机系； 重庆大学计算机学院；

【摘要】 <正>1引言近年来,离群挖掘已经成为数据挖掘与知识发现领域的一个研究热点,它探讨如何从大量数据中挖掘出离群对象(Outlier),即与多数常规数据对象更多还原

【Abstract】 Detecting and analyzing for outliers is of great importance in many applications,including telecom fraud detection,disease diagnosis,and network invasion detection,etc.Moreover,mass of these fields require good realtime performance.The key attribute subspace of outliers is help to find out the extended knowledge of identified outliers, such as their origin and features.After discussing the relation of outlying individual and its outlying attribute values,this paper proposes a real-time searching algorithm for key attribute subspace of outliers based on data exploration analysis mode.Experimental results show that the approach is scalable and it can efficiently satisfy the demand of real-time outlier analysis.更多还原