Kernel-based Adaptive K-medoid Clustering and its Application in Intrusion Detection
基于核的自适应聚类及其在入侵检测中的应用

Aiming at the weakness of k-medoid algorithm that it is unable effectively to cluster large data set and high-dimension data,kernel-based learning method was introduced to the k-medoid clustering algorithm,kernel-based adaptive k-medoid algorithm was proposed,and it can cluster large data set and high-dimensional data.The results of experiment using the data sets of KDD cup 99 demonstrate that the algorithm has excellent capability and applying it in intrusion detection system can be an effective way.