基于聚类的离群点检测方法研究综述  

Survey of Outlier Detection Methods Based on Clustering

作　　者：周玉 朱文豪 房倩 白磊 ZHOU Yu;ZHU Wenhao;FANG Qian;BAI Lei(School of Electric Power,North China University of Water Resources and Electric Power,Zhengzhou 450011,China)

机构地区：[1]华北水利水电大学电力学院,郑州450011

出　　处：《计算机工程与应用》2021年第12期37-45,共9页Computer Engineering and Applications

基　　金：河南省高等学校青年骨干教师培养计划(2018GGJS079);国家自然科学基金(U1504622,31671580)。

摘　　要：离群点检测在数据处理中具有重要研究意义,其检测方法大致可以分为基于统计、基于距离、基于密度和基于聚类的方法。为了及时掌握当前基于聚类技术的离群点检测方法的研究现状,通过归纳与整理,将具有代表性的基于聚类的离群点检测方法进行了介绍和归类,将其主要分为静态数据集中的检测方法、数据流中的检测方法、大规模数据中的检测方法和其他方法等四大类。对每类方法所解决的问题、算法思想、应用场景以及各自的优缺点进行了详细的归纳和分析,指出目前存在的问题以及未来发展方向。Outlier detection has great significance in data processing.At present,its detection methods can be roughly divided into statistical based,distance based,density based and clustering based.In order to grasp the current research sta-tus of outlier detection methods based on clustering technology,this paper introduces and classifies the representative out-lier detection methods based on clustering,which are mainly divided into four categories:detection methods in static data sets,detection methods in data streams,detection methods in large-scale data and other methods.The problems,algorithm ideas,application scenarios and their advantages and disadvantages of each method are summarized and analyzed in detail,and the existing problems are analyzed and the future development direction is proposed.

关 键 词：离群点检测 聚类 静态数据集 数据流 大规模数据集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]