计算机与数字工程2017，Vol.45Issue(8)：1473-1478,1508,7.DOI:10.3969/j.issn.1672-9722.2017.08.003
基于组合增量聚类的数据流异常检测研究
Research on Data Stream Amonaly Detection Using Incremental Clustering Ensemble
摘要
Abstract
Anomaly detection in data stream has gained a high attraction due to its applications,including real-time surveil-lance,network intrusion detection. However,traditional clustering is no longer suitable due to the particularity and timeliness of the data stream and the continuous characteristics of the data flow. Therefore,incremental clustering has become the research hotspots towards anomaly detection in data stream. An anomaly detection model in data stream is proposed based on two improved incremen-tal clustering aiming at the problem of low efficiency,high false positives and lack of pertinence of single clustering. The mothod is based on improved incremental clustering and an effective consensus function is designed to merge the results of a variety of cluster-ing algorithms. The experimental results show that the improved clustering algorithms are applicable to incremental clustering and they have better efficiency and better clustering result than single clustering method.关键词
数据流/异常检测/增量聚类/子空间聚类/聚类融合Key words
data stream/anomaly detection/incremental clustering/subspace clustering/clustering ensemble分类
信息技术与安全科学引用本文复制引用
许福,徐建..基于组合增量聚类的数据流异常检测研究[J].计算机与数字工程,2017,45(8):1473-1478,1508,7.基金项目
国家自然科学基金项目"虚拟计算环境下的软件自愈机理和方法研究"(编号:61300053)资助. （编号:61300053）
