计算机工程2013，Vol.39Issue(2)：178-181,4.DOI:10.3969/j.issn.1000-3428.2013.02.036
基于小波密度估计的数据流离群点检测
Outliers Detection in Data Stream Based on Wavelet Density Estimation
刘耀宗 1张宏 2孟锦 1韩法旺1
作者信息
- 1. 南京理工大学计算机学院,南京210094
- 2. 南京森林警察学院信息系,南京210046
- 折叠
摘要
Abstract
In order to find local outliers in data stream, this paper analyzes traditional outliers detection algorithms, and introduces an outlier detection algorithm based on Wavelet Density Estimation(WDE). It uses a multi-scale and multi-granularity characteristics of WDE using the wavelet probability threshold to judge the data stream within the current sliding window data points as outliers, and discusses outlier detection in data stream process. Simulation results show that this algorithm has higher detection efficiency and accuracy in the data stream than Kernel Density Estimation(KDE) algorithm.关键词
数据流/局部离群点/离群点检测/核密度估计/小波密度估计Key words
data stream/ local outlier/ outlier detection/ Kernel Density Estimation(KDE)/ Wavelet Density Estimation(WDE)分类
信息技术与安全科学引用本文复制引用
刘耀宗,张宏,孟锦,韩法旺..基于小波密度估计的数据流离群点检测[J].计算机工程,2013,39(2):178-181,4.
