Existing works of reduced support vector set method find the reduced set vectors based on solving an unconstrained optimization problem with multivariables,which may suffer from numerical instability or get trapped in a local minimum.In this paper,a reduced set method relying on kernel-based clustering is presented to simplify SVM(support vector machine)solution.The method firstly organizes support vectors in clusters in feature space, and then,it finds the pre-images of the cluster centroids in feature space to construct a reduced vector set.This approach is conceptually simpler,involves only linear algebra and overcomes the difficulties existing in the former reduced set methods.Experimental results on real data sets indicate that the proposed method is effective in simplifying SVM solution while preserving machine's generalization performance.