%0 Journal Article
%T Sample-adaptive-parameters outlier detection method for associated-attributes<br>适用于关联属性的样本自适应参数孤立点检测法
%A LIU Sheng-zong
%A FAN Xiao-ping
%A LIAO Zhi-fang
%A <br>刘胜宗
%A 樊晓平
%A 廖志芳
%J 计算机应用研究
%D 2012
%I 
%X In order to solve the interfering problem of associated-attributes in datasets, this paper improved the traditional k-nearest neighbor outlier detection method by the introduction of Mahalanobis distance, and proposed a new sample-based parameters selection method which gained the optimization k-distance value and threshold by training the normal and outlier data in the sample dataset. Simulation results illustrate the proposed algorithm has higher accuracy, lower false detection rate.
%K outlier detection
%K associated-attributes
%K sample-adaptive
%K Mahalanobis distance<br>孤立点检测
%K 关联属性
%K 样本自适应
%K Mahalanobis距离
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=9B3C227A81E2A28CEFF93A377703E50C&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=9CF7A0430CBB2DFD&sid=02F9B432D1C44D18&eid=304004327C104506&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10