摘要
针对K-means算法全局搜索能力不足,提出基于人工鱼群的优化K-means聚类算法(AFS-KM),该算法克服了K-means聚类算法对初始聚类中心选择的敏感问题,能够获得全局最优的聚类划分。在聚类过程中,采用一种基于信息增益的属性加权的实体之间距离计算方法进行聚类划分时,对于球形数据和椭球形数据都能够获得理想的聚类划分结果。对KDD-99数据集的仿真实验结果表明,该算法在网络入侵检测时获得了理想的检测率和误报率。
Aimed at the lack of global search capability of K-means algorithm,optimized K-means clustering algorithm based on artificial fish swarm（AFS-KM）was presented in this paper,which can overcome the problem of initial clustering center selection sensitivity of K-means and can obtain global optimized clustering partition.During clutering process,a weighted distance computation method based on information gain attribute weighting is used,so,the better clustering can be obtained for both spherical data and ellipsodal data.Simulation experiment is implemented over data set KDD-99,and the result shows that the satisfying detection rate and false acceptance rate can be obtained in network intrusion detection.
出处
《计算机科学》
CSCD
北大核心
2012年第12期60-64,共5页
Computer Science
基金
黑龙江省教育厅科技研究项目(11553001)资助
关键词
聚类
人工鱼群
信息增益
属性加权
入侵检测
Clustering
Artificial fish swarm
Information gain
Attribute weighting
Intrusion detection
