期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2010
卷号：2
期号：5
页码：1865-1870
出版社：Engg Journals Publications
摘要：Clustering is the one of the efficient datamining techniques for intrusion detection. In clustering algorithm kmean clustering is widely used for intrusion detection. Because it gives efficient results incase of huge datasets. But sometime kmean clustering fails to give best result because of class dominance problem and no class problem. So for removing these problems we are proposing two new algorithms for cluster to class assignment. According to our experimental results the proposed algorithm are having high precision and recall for low class instances.
关键词：Feature selection; k-mean clustering; fuzzy k mean clustering; and KDDcup 99 dataset
