%0 Journal Article
%T A Fast Outlier Detection Algorithm for Data Streams Based on Dynamic Grids<br>基于动态网格的数据流离群点快速检测算法
%A YANG Yi-Dong
%A SUN Zhi-Hui
%A ZHU Yu-Quan
%A YANG Ming
%A ZHANG Bo-Li
%A <br>杨宜东
%A 孙志挥
%A 朱玉全
%A 杨明
%A 张柏礼
%J 软件学报
%D 2006
%I 
%X As an important task of data mining, outlier detection has been applied to many fields. Recently, research on mining in data stream is receiving more and more attention. For solving outlier detection in data stream, a new fast outlier detection algorithm is presented. Based on dynamically grid partitioning data space, the method separates dense areas from sparse areas. Data in dense areas are filtered simply, which reduces greatly the size of objects the algorithm should consider. Outliernesses of candidates in sparse areas are approximated efficiently. Data with high outlierness are outputted as outliers. Results of experiments on synthetic and real data sets show promising availabilities of the approaches.
%K data stream
%K outlier detection
%K time-sensitive dynamic grids partitioning<br>数据流
%K 离群点检测
%K 时间相关动态网格划分
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=63BFD8B72E594CC2&yid=37904DC365DD7266&vid=BCA2697F357F2001&iid=5D311CA918CA9A03&sid=7E823D8841E016EC&eid=E35B85FD10020A32&journal_id=1000-9825&journal_name=软件学报&referenced_num=7&reference_num=15