关键词: 直觉模糊聚类, 核空间, 局部信息

Abstract: In view of the shortcomings of the existing intuitionistic fuzzy C-means clustering, nonlinear function is adopted to map data samples from Euclidean space to high dimensional feature space of Hilbert, and kernel space intuitionistic fuzzy clustering algorithm is gotten. At the same time, taking account the interaction of neighboring pixels, the neighborhood pixels are integrated into objective function optimization of kernel space intuitionistic fuzzy clustering algorithm, and kernel space intuitionistic fuzzy clustering segmentation with pixels local information is obtained by mathematical deduction. The test results of graph segmentation show that kernel space intuitionistic fuzzy C-means clustering algorithm is more satisfactory in segmentation results compared with the existing intuitionistic fuzzy C-means clustering segmentation method, and the kernel space intuitionistic fuzzy C-means segmentation method with local information is shown to be more robust.

Key words: intuitionistic fuzzy clustering, kernel space, local information