The Improvement and Application of a K-Means Clustering Algorithm

被引：0
|
作者：
Tao, Li Jun [1 ]
Hong, Liu Yin [1 ]
Yan, Hao [1 ]
机构：
[1] Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2016 IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2016) | 2016年
关键词：
clustering algorithm; improved K-means algorithm; intrusion detection;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a K-means algorithm with the dynamic adjustable number of clusters. The algorithm uses the improved Euclidean distance formula to calculate the distance between the cluster center and data, by judging whether the distance is greater than the threshold to automatically adjust the number of clusters. Finally, the improved algorithm is applied to intrusion detection system to detect unknown attacks. The test results shows, Compared with traditional Kmeans algorithm, the K-means algorithm with the dynamic adjustable number of clusters has a better effect.
引用
收藏
页码：93 / 96
页数：4
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