摘要
支持向量聚类(SVC)是在支持向量机的思想上发展而来一种聚类方法,针对其处理大规模数据集速度缓慢的缺点,提出了一种改进的分块支持向量聚类算法。改进的算法分为三个阶段:前期的预处理,中期的分块算法,后期的改进标类算法。提出的方法显著加快了SVC的速度,在保持原来SVC算法的优点的基础上,对大规模数据集以及非均匀分布数据集等具有良好的效果。将其应用到网络入侵检测,实验结果表明改进的算法行之有效。
The Support Vector Clustering （SVC） algorithm is inspired by support vector machines. Aiming at its defection of slowly training of large-seale sets, an improved algorithm of chunking SVC is proposed to speed the algorithm. The improvement contains three stages： firstly pre-processing , secondly chunking algorithm and finally improved cluster labeling method. The proposed method strongly enhanced the SVC. Keeping the advantages of original SVC, it has an excellent performance to deal with large-scale sets as well as unevenly distributed data sets. Applied the algorithm to network intrusion detection, the result shows that it＇s acceptable.
出处
《微计算机信息》
北大核心
2006年第05X期46-49,共4页
Control & Automation
基金
教育部科学技术研究基础条件平台建设项目(编号:505006)
关键词
支持向量聚类
数据挖掘
网络安全
入侵检测
support vector clustering （SVC）
data mining
network security
intrusion detection
