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《Intelligent Computer and Applications》 2019-04
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Outlier detection methods based on natural nearest neighbors

LI Shiguo;LU Jianyun;DENG Jianxun;Big Data Business Section,CISDI Information Technology Co.,Ltd;School of AI and Big Data,Chongqing College of Electronic Engineering;  
Near neighbor technique has been one of the important technologies in machine learning and data mining. It has the characteristics of simplicity,speed and efficiency in practical applications,which is favored by researchers. The existing near neighbor technologies include k-nearest neighbors,reverse k-nearest neighbors,mutual k-nearest neighbors,shared k-nearest neighbors,and natural nearest neighbors. In order to gain a comprehensive understanding of the characteristics of several near neighbor technologies,the mentioned near neighbor technologies are compared and analyzed. First,this article gives a comparative analysis of the near neighbor technologies and points out the special characteristics; Second,the concept of natural nearest neighbors is applied into other near neighbor technologies and natural reverse nearest neighbors,natural mutual nearest neighbors,and natural shared nearest neighbors are proposed; Finally,the proposed three algorithms are compared experimentally in outlier detection. The experimental results showthat natural reverse nearest neighbors and natural mutual nearest neighbors can effectively discover local and global outliers.
【Fund】: 重庆市教委2018科技青年项目(KJQN201803109)
【CateGory Index】: TP181;TP311.13
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