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题名: Outlier detection based on Linear programming
作者: Gao EY(高恩阳) ; Liu WJ(刘伟军) ; Wang TR(王天然) ; Deng HB(邓华波)
作者部门: 装备制造技术研究室
会议名称: 2012 International Conference on Information Engineering
会议日期: June 27-28, 2012
会议地点: Singapore
会议录: Lecture Notes in Information Technology
会议录出版者: Springer Verlag
会议录出版地: Heidelberg, Germany
出版日期: 2012
页码: 194-196
ISBN号: 978-1-61275-024-8
关键词: Outlier detection ; Markov model ; Linear programming
摘要: Outlier detection is an important step in many data-mining applications. In this paper, we propose an outlier detection mathod based on Linear Programming. The essential idea behind this technique is that two neighbor data points must be normal points or outliers in the same time, this is consistent with Markov property, hence we construct k-nearest neighbor graph model. As the main result of this paper, we show that Linear Programming method can detect outliers correctly, even if the data has outliers that form a small cluster, in contrast to state of the art outlier detection algorithm LOF.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/10151
Appears in Collections:装备制造技术研究室_会议论文

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Recommended Citation:
高恩阳; 刘伟军; 王天然; 邓华波.Outlier detection based on Linear programming.见:Springer Verlag.Lecture Notes in Information Technology,Heidelberg, Germany,2012,194-196
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