核聚类算法

该文提出了一种用于聚类分析的核聚类方法，通过利用Mercer核，作者把输入空间的样本映射到高维特征空间后，在特征空间中进行聚类，由于经过了核函数的映射，使原来没有显现的特征突出来，从而能够更好地聚类，该核聚类方法在性能上比以典的聚类算法有较大的改进，具有更快的收敛速度以及更为准确的聚类，仿真实验的结果证实了核聚类方法的可行性和有效性。

Kernel Clustering Algorithm

A new clustering algorithm is proposed for cluster analysis in this paper. 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. We adopt the traditional clustering methods and the kernel technique to construct our kernel clustering algorithm. By using Mercer kernel functions, we can map the data in the original space to a high dimensional feature space in which we can perform clustering 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 show the feasibility and effectiveness of the kernel clustering algorithm.