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A Linear Programming Framework for Outlier Detection
Authors
刘伟军
王天然
高恩阳
Publication date
1 January 2013
Publisher
Abstract
孤立点检测是数据挖掘中的重要问题,可以发现不具备一般特性的数据,进而发现潜在的有用信息。现有的孤立点检测算法对于孤立点组成小集群的情形,一般不能正确检出。针对这一问题,提出一种新的基于线性规划的孤立点检测方法,该方法基于一个简单的事实:紧邻的两个数据点,必然同时为孤立点或正常点。首先建立待检测数据点的图模型,通过构造顶点能量模型和边模型,建立孤立点检测问题的马尔科夫模型,之后通过求解线性规划问题,得到该模型的最优解,进而得到孤立点检测结果。最后,使用一个合成数据集和三个真实数据集进行实验,验证本文所提出的算法,实验结果表明,提出的算法对于普通数据集和含有孤立点组成小集群的数据集,都能够正确地检出,且具有较高的检测正确率
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Shenyang Institute of Automation,Chinese Academy Of Sciences
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oai:ir.sia.cn/:173321/14634
Last time updated on 12/02/2018
Institutional Repository of Institute of Automation, CAS
See this paper in CORE
Go to the repository landing page
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oai:ir.sia.cn/:173321/14634
Last time updated on 29/11/2016