一种自动聚类的离群点识别方法研究  

Research on an Automatic Clustering Outlier Recognition Method

作　　者：黄强 叶青[1] 聂斌[1] 朱彦陈[1] 郭永坤 HUANG Qiang;YE Qing;NIE Bin;ZHU Yanchen;GUO Yongkun(School of Computer Science,Jiangxi University of Chinese Medicine,Nanchang 330004,China)

机构地区：[1]江西中医药大学计算机学院,江西南昌330004

出　　处：《现代信息科技》2023年第7期6-10,共5页Modern Information Technology

基　　金：国家自然科学基金(62141202,82260988)。

摘　　要：针对传统离群点识别方法对数据的分布形状和密度有特定要求,需设定参数的问题,提出了一种自动聚类的离群点识别方法。该方法通过引入相互K近邻数来表示数据对象的离群度,对数据的分布形状、分布密度无要求,可以输出全局离群点、局部离群点和离群簇;通过k次迭代来实现自动聚类,无需人为设定参数。通过合成数据以及UCI数据实验,验证了该方法的有效性、普适性。Aiming at the problem that traditional outlier recognition methods have specific requirements for the distribution shape and distribution density of data and need to set parameters,an automatic clustering outlier recognition method is proposed.This method represents the outlier degree of data objects by introducing the mutual K-nearest neighbor number,with no requiring for the distribution shape and distribution density of the data,and can output global outliers,local outliers,and outlier cluster;Automatic clustering is achieved through k iterations,no need to manually set parameters.The effectiveness and universality of this method are verified through synthetic data and UCI data experiments.

关 键 词：局部离群点 离群点识别 离群簇 自动聚类 数据挖掘 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]