Chinese Journal of Computers   Full Text
Title	Kernel clustering algorithm
Authors	ZHANG Li/ZHOU Wei-da/JIAO Li-cheng
Address
Year	2002
Issue	No.6(587-590)
Abstract &   Background	A new clustering algorithm is proposed for cluster analysis. In general, the reliability of the traditional clustering algorithms strictly depends on the feature difference of data. If the feature differences are large, it is easy to implement clustering. But if the feature differences are small and even cross in the origin space, it is difficult for traditional algorithms to clustering correctly. Authors adopt the traditional clustering methods and the kernel technique to construct own kernel clustering algorithm. By using Mercer kernel functions, the data in the original space can be mapped to a high-dimensional feature space in which clustering can be performed efficiently. The features of kernel clustering algorithm are fast in convergence speed and accurate in clustering, compared with classical clustering algorithms. The results of simulation experiments demonstrate the feasibility and effectiveness of the kernel clustering algorithm. keywords Clustering analysis, Kernel function, Feature space