离群点检测技术综述  

Overview of outlier detection technology

作　　者：梅林 张凤荔[1] 高强[1] Mei Lin;Zhang Fengli;Gao Qiang(School of Information&Software Engineering,University of Electronic Science&Technology of China,Chengdu 610054,China;School of Computer Science&Technology,Southwest Minzu University,Chengdu 610225,China)

机构地区：[1]电子科技大学信息与软件工程学院,成都610054 [2]西南民族大学计算机科学与技术学院,成都610225

出　　处：《计算机应用研究》2020年第12期3521-3527,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61472064,61602096);四川省科技计划资助项目(2018GZ0087,2016FZ0002);中央高校基本科研业务费专项基金资助项目(2017NZYQN26)。

摘　　要：为了深入了解离群点检测技术发展状况,对近年离群点检测技术进行综述,首先介绍与总结了离群点的定义、引起离群的原因和离群点挖掘算法的分类;其次,对基于邻近性的离群点检测算法、分布式架构下的离群点检测算法以及基于深度学习的离群点检测算法进行综述与总结,尤其对该领域目前最有代表性的方法进行了探讨,指出了其优缺点;最后展望了离群点检测技术未来的研究方向。In order to understand the development of outlier detection technique detailly,this paper overviewed the outlier detection techniques in recent years.Firstly,it introduced and summarized the definition of outliers,the causes of outlier and classification of the outlier mining algorithms.Next,it overviewed outlier detection methods based on neighbor,based on distributed framework and based on deep learning.Especially,it discussed the most representative methods in outlier detection,and pointed out the merits and drawbacks.In the end,this paper concluded and discussed the future direction of outlier detection techniques.

关 键 词：离群点 深度学习 监督学习 半监督学习 大数据 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]