一种改进的k-means聚类算法在入侵检测中的应用

Application of Modified k-means Clustering Algorithm in Intrusion Detection

讨论了经典的k-平均聚类算法,说明了它存在不能很好地处理符号数据和对噪声与孤立点数据敏感等不足,提出了一种改进的k-平均聚类算法,克服了k-平均聚类算法的缺点,并从理论上分析了该算法的复杂度。实验证明,用该方法实现的数据聚类与传统的基于平均值的方法相比较,能有效提高数据聚类效果以及入侵检测的准确度。

The classic algorithm of k-means is discussed,the shortages of this algorithm such as it can not deal with symbolic data and it is sensitive for data of isolation point and noise are demonstrated.A modified k-means clustering algorithm is put forward,it changes the shortcomings of k-means.Its complexity is analyzed from theoretical.The experiments show that,compared with traditional method based on means,the modified data clustering algorithm can improve the efficiency of data clustering and the accuracy of intrusion detection.