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。
关键词:
入侵检测系统,
数据挖掘,
异常记录,
聚类算法
Abstract: 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.
Key words:
intrusion detection system,
data mining,
anomaly record,
clustering algorithm
中图分类号:
陈小辉. 基于数据挖掘算法的入侵检测方法[J]. 计算机工程, 2010, 36(17): 72-73,76.
CHEN Xiao-Hui. Intrusion Detection Method Based on Data Mining Algorithm[J]. Computer Engineering, 2010, 36(17): 72-73,76.