文章基本信息

标题：An Efficient Parallel Anomaly Detection Algorithm Based on Hierarchical Clustering
本地全文：下载
作者：Wei-wu, Ren ; Liang, Hu ; Kuo, Zhao 等
期刊名称：Journal of Networks
印刷版ISSN：1796-2056
出版年度：2013
卷号：8
期号：3
页码：672-679
DOI：10.4304/jnw.8.3.672-679
语种：English
出版社：Academy Publisher
摘要：For the purpose of improving real time and profiles accuracy, a parallel anomaly detection algorithm based on hierarchical clustering has been proposed. Training and predicting are two busiest processes and they are parallel designed and implemented. Moreover, an abnormal cluster feature tree is built to dig anomalies from normal profiles. A series of experiment results on well-known KDD Cup 1999 data sets indicate that the improved algorithm has superior performance in both detection and real time.
关键词：parallel algorithm;hierarchy clustering;abnormal cluster feature tree;normal profiles