多核模糊聚类

针对单核聚类的性能局限性问题，提出将高斯核、Sigmoid核以及多项式核等多种核组成一种新的多核函数，并利用于模糊核进行聚类。高斯核在聚类中有广泛应用，同时Sigmoid核在神经网络中被证明具有很好的全局分类性能。将不同的核函数组合起来的多核函数将结合各种核函数的优点，其聚类性能优于利用单核的模糊核聚类（KFCM），实验结果表明了该方法的有效性。

Multiple kernel fuzzy clustering

Because the fuzzy clustering based on single kernel function has some limitation on performance, a new multiple-kernel fuzzy clustering is put forward. The new multiple kernel is constituted by Gaussian kernel, Sigmoid kernel and polynomial kernel. Gaussian kernel is wildly used in KFCM. It can be demonstrated that sigmoid kernel derived from neural network has good global classification performance, as well as polynomial kernel. The new multiple kernel combines the advantage of them. The experimental results prove that the fuzzy clustering with multiple kernel is much better than with single kernel on performance.