VDOD:Distributed Outlier Detection Algorithm Based on KD-tree
LI Zimao
LUO Qing
LIU Jing
Abstract：A new distributed outlier detection algorithm,VDOD is proposed for large data of the large amount of data and high dimensional characteristics.In the data preprocessing stage,a data partitioning method based on variance is proposed.KD tree is es?tablished in the process of partitioning,and the data are evenly distributed to each computing node through KD tree.In the outlier detection stage,batch filtering is performed by R tree.Finally,the validity of the VDOD algorithm is verified based on the real data set and the artificial data set.The experimental results show that compared with the existing algorithms,the algorithm can signifi?cantly improve the computational efficiency and significantly reduce the network overhead.
Keywords：distributedoutlier detectionlarge dataKD tree
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 419-423,428 )