核可能性C-均值(KPCM)聚类算法将核方法引入可能性聚类中,使其对超球体、含噪声和奇异点的数据能进行有效聚类,但存在可能性聚类的中心重合问题。因此,将β-截集引入KPCM聚类算法中,通过产生聚类核修改部分样本数据的典型值,以改善类间关系。同时,提出了一种基于截集门限的核可能性C-均值(C-KPCM)聚类算法,克服了KPCM聚类算法一致性聚类的缺陷。结合图像的非局部空间信息,利用自适应中值滤波算法可自适应调节滤波半径的特性,产生新的模糊因子,并将其加入C-KPCM聚类算法的目标函数中,提出了基于非局部空间信息的核可能性C-均值聚类算法,增加了强噪声干扰下聚类算法的鲁棒性,仿真结果验证了本算法有效性。

Kernel possibilistic C-means (KPCM) clustering algorithm introduces kernel method into possibilistic clustering, which can effectively cluster hypersphere, noisy, and singular point data, but it has the center coincidence problem of possibilistic clustering. Therefore, the β cut-set is introduced into the KPCM clustering algorithm, and the typical values of some sample data are modified by generating clustering kernel to improve the relationship between classes. At the same time, a Kernel possibilistic C-means clustering algorithm based on cut-set threshold (C-KPCM) is proposed to overcome the defect of consistency clustering of KPCM clustering algorithm. Combined with the non-local spatial information of the image, the adaptive median filtering algorithm is used to adaptively adjust the filtering radius to generate new fuzzy factors, which are added to the objective function of C-KPCM clustering algorithm. The kernel possibilistic C-means clustering algorithm based on non-local spatial information enhances the robustness of the clustering algorithm under strong noise interference. Simulation results verify the effectiveness of the proposed algorithm.