计算机工程2018，Vol.44Issue(1)：51-55,5.DOI:10.3969/j.issn.1000-3428.2018.01.008
基于高维数据流的异常检测算法
Anomaly Detection Algorithm Based on High-dimensional Data Stream
摘要
Abstract
The traditional outlier detection algorithm based on Euclidean distance can not guarantee the accuracy and the running time is too long in high-dimensional data detection.Based on the characteristics of high-dimensional data flow,an improved outlier detection algorithm based on angular variance is proposed by using the method of angle variance.The optimal data set grid and the nearest data grid are constructed to calculate the small scale data flow to measure the abnormal degree of the latest data points,and the improved algorithm is used to the elevator real data flow detection in wireless sensor network acquisition to achieve elevator fault detection.Experimental results show that compared with ABOD and HODA algorithms,the improved algorithm can effectively identify abnormal points in high-dimensional data streams and can be applied to high-dimensional data streams with high real-time requirements.关键词
数据挖掘/高维数据流/异常检测/海量数据/角度方差Key words
data mining/high-dimensional data stream/anomaly detection/massive data/angle variance分类
信息技术与安全科学引用本文复制引用
余立苹,李云飞,朱世行..基于高维数据流的异常检测算法[J].计算机工程,2018,44(1):51-55,5.基金项目
国家自然科学基金(61201212,61272449). （61201212,61272449）
