Research The Kernel Clustering Algorithm And Its Application

Data mining is an important subject of information field. Data mining technique iswidely used in many industries now. Clustering is one of the core technique of data mining.K-means clustering, which is widely used in presently clustering algorithms, is a concise andpractical algorithm, but it didn't optimize the features of the data samples and so the result of the clustering is not satisfying, if the boundaries of the samples is nonlinear or the samples doesn't subject to Gaussian distribution. The kernel method improves the optimization of the example features and transforms the nonlinear learning problems to linear learning problems by mapping the samples from input space to feature space. By introducing the kernel methodsthe clustering algorithm can obtain better performance.We present a Sectional Set FKCM which is a generalization of the conventional Sectional Set FCM and HCM. The main idea of The algorithm is how to map sample data to feature space,then to compute the center and degree of membership of all categories .so the fuzzy clustering can be processed in the feature space.meanwhile adding cut factorλto some sample(process close degree of membership)to conform their adscription and ensure a good effect of clustering .The results of experiments on real data show that the Sectional Set fuzzy kernel C-means clustering algorithm can effecttively cluster on data with diversiform structures in contrast to the Sectional Set fuzzy C-means clustering algorithm.The algorithm owns better performance than classical clustering algorithm (HCM,FCM,FKCM).Finally,we apply this clustering to intrusion detection.because of a good clustering effect of the algorthm,instrusion attack can be detected availably ,through modeling experiment the validity of the algorithm to intrusion detection is testified.