多路径高斯核模糊C均值聚类算法

聚类算法单一迭代路径限制了参数优值的搜索。提出一种多路径高斯核模糊C均值聚类算法（MGKFCMs），MGKFCMs算法首先取核目标函数及模糊隶属度函数中的核函数为高斯核函数；然后利用梯度法得到聚类中心迭代公式，并基于该迭代公式和粒子群算法作聚类中心的并行参数迭代，在每一次聚类迭代时，选择聚类目标函数值小的路径作为参数迭代最终路径。对比分析了MGKFCMs算法的相关性质，通过仿真实验验证了所提算法的有效性。

A multi-path Gaussian kernel fuzzy C means clustering algorithm

The single iteration path of the clustering algorithm limits the search path of the parameter’s optimal value. In this paper, a multi-path Gaussian kernel fuzzy c-means clustering method is proposed and named MGKFCMs. Firstly, MGKFCMs takes the nuclear objective function and the fuzzy membership degree function in the kernel function as the Gaussian kernel function. Secondly, the gradient method is used to get the iterative formula of the clustering center. Based on this iterative formula and particle swarm optimization algorithm, the parameters of the clustering center are calculated iteratively in parallel. In every iteration of clustering, a path with small clustering objective function value is selected as the final path of parameter iteration. The correlation property of MGKFCMs is analyzed, and the convergence of the algorithm is studied. Simulation results show that the proposed algorithm is effective.