Based on the analysis of the core concepts of the kernel methods, a clustering algorithm based on kernel methods was put forward. In general, traditional clustering algorithms are suitable to implement clustering only if the feature differences of data are large. If the feature differences are small and even cross in the original space, it is difficult for traditional algorithms to cluster correctly. By using kernel functions, the data in the original space was mapped into a high-dimensional feature space, in which more features of the data were exposed so that clustering could be performed efficiently. Compared with the traditional clustering methods, this clustering method had superiorities in dealing with the nonlinear data, which made its clustering result more objective and valid. This method was applied to the classification of 16 groups of data, and results show the feasibility and effectiveness of the kernel clustering algorithm.
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