摘要
K-Means和DBSCAN算法初始聚类中心的选择对数据挖掘结果的影响较大。针对上述问题,利用信息熵改进初始聚类中心选择方法,提高数据挖掘效率。将改进的K-Means算法与DBSCAN算法结合应用于入侵检测系统,对一个通用检测记录集进行异常检测测试,实验结果证明了该方法的有效性。
How to select original clustering cores of K-Means and DBSCAN is important to the result of data mining. Aiming at the problem, this paper improves the method of selecting original clustering cores via entropy. It applies improved K-Means and DBSCAN to the intrusion detection system, and does anomaly detection test on a common set of records in the system. Experimental result proves that the method is effective.
出处
《计算机工程》
CAS
CSCD
北大核心
2010年第17期72-73,76,共3页
Computer Engineering
关键词
入侵检测系统
数据挖掘
异常记录
聚类算法
intrusion detection system
data mining
anomaly record
clustering algorithm
