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题名: 一种基于线性规划的孤立点检测方法
其他题名: A Linear Programming Framework for Outlier Detection
作者: 高恩阳 ; 刘伟军 ; 王天然
作者部门: 装备制造技术研究室
关键词: 线性规划 ; 孤立点检测 ; 马尔科夫模型
刊名: 控制工程
ISSN号: 1671-7848
出版日期: 2013
卷号: 20, 期号:6, 页码:1123-1126, 1130
收录类别: CSCD
产权排序: 1
摘要: 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。
英文摘要: Outlier detection is an important step in many data - mining applications. It can find patterns in data that do not conform to expected behavior,these nonconforming patterns can imply potentially useful information. The disadvantages of current methods are that if the data has outliers that form a small cluster,the technique fails to label them correctly. In this paper,we propose a new method for outlier detection. The essential idea behind this technique is that two neighbor data points must be normal points or outliers in the same time. The paper first create the graph model of the data points to be detected. By constructing energy model of vertices and edges,the Markov model for outlier detection problem is established,followed by solving a linear programming problem,the optimal solution of the model is obtained ,and then outlier detection results are provided. Finally,the paper use a synthetic data set and three real data sets experiment to test the proposed algorithm,experiment results show that the proposed algorithm for ordinary data sets and the data sets containing small cluster of data sets are able to correctly detection,and has a high detection accuracy.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/14634
Appears in Collections:装备制造技术研究室_期刊论文

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