计算机应用研究2016，Vol.33Issue(11)：3383-3386,4.DOI:10.3969/j.issn.1001--3695.2016.11.040
基于角度方差的多层次高维数据异常检测算法
Hybrid outlier detection algorithm based on angle variance for high-dimensional data
摘要
Abstract
Outlier detection is a major task of data mining.Outlier detection methods based on Euclidean distances are not ca-pable for high-dimensional data because they can hardly ensure the cost of the computation and the accuracy.After analyzing angle-based outlier detection method,this paper proposed a novel approach called hybrid outlier detection algorithm based on angle variance for high-dimensional data.The algorithm first utilized rough set theory to analyze the impact between the attri-butes and abandoned less important ones.Then it divided data into different cubes according to the distribution of data on every attribute.It only focused on the cubes with high possibility to contain outliers.At last,through the calculation of angle-based outlier factor,it was able to detect outliers.Compared to conventional algorithms,such as ABOD,FastVOA and LOF, the experimental results verify the feasibility of the proposed approach in terms of both efficiency and accuracy.关键词
高维数据/异常检测/降维/网格/角度方差Key words
high-dimensional data/outlier detection/dimensional reduction/grid/angle variance分类
信息技术与安全科学引用本文复制引用
陈圣楠,钱红燕,李伟..基于角度方差的多层次高维数据异常检测算法[J].计算机应用研究,2016,33(11):3383-3386,4.基金项目
中国民航大学中国民航信息技术科研基地资质项目 （）
