Abstract: To solve the problem of high cost in labeling the data artificially and that of the dimension effect by traditional clustering method, this paper proposes a new fuzzy support vector clustering algorithm to cope with unlabeled data. Through combining K-means and DBSCAN algorithm to generate association matrix, setting the threshold value of constraint term to get the initial clustering, and using the fuzzy support vector domain description, the final result is achieved. The contrast experiment shows the feasibility and effectiveness of this method.

Key words: network intrusion detection, fuzzy kernel clustering, support vector

摘要： 针对人工标记数据类别代价太高以及传统聚类方法在处理高维数据时产生的维度效应，提出了一种针对无标签数据的新型模糊核聚类方法。通过将K-means与DBSCAN聚类算法相结合生成关联矩阵，设置约束条件的阈值得到初始聚类结果，并在模糊支持向量数据描述方法的基础上完成聚类过程。通过在网络连接数据的对比实验，验证了该方法的可行性与有效性。

关键词: 网络入侵检测, 模糊核聚类, 支持向量