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Outlier detection based on Linear programming
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
会议录名称Lecture Notes in Information Technology
出版者Springer Verlag
出版地Heidelberg, Germany
2012
页码194-196
产权排序1
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
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/10151
专题装备制造技术研究室
作者单位1.Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, 110016, China
2.Graduate School of The Chinese Academy of Sciences, Beijing, 10049, China
3.Shenyang Jianzhu University, Shenyang, 110168, China
推荐引用方式
GB/T 7714
Gao EY,Liu WJ,Wang TR,et al. Outlier detection based on Linear programming[C]. Heidelberg, Germany:Springer Verlag,2012:194-196.
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