A new general equalization fuzzy C-means clustering algorithm that targets the shortcomings of existing

non-convergent types is proposed and applied in image segmentation. The proposed general equalization fuzzy clustering algorithm is also extended into the Hilbert reproduced kernel space. This approach can improve the universality of this algorithm class. The limit expression properties of the Schweizer T-norm are applied to construct the objective function of the new general equalization fuzzy C-means clustering based on the objective function of existing types. The Lagrange multiplier method is then adopted to obtain iterated formulae of the fuzzy membership and clustering center for the modified general equalization fuzzy C-means clustering. The iterative expression of the clustering center is modified to further improve the performance of the clustering algorithm. The modified clustering algorithm significantly improves a clustering performance class. Finally

a nonlinear function is adopted to map data samples from the Euclidean space to the high-dimensional feature space of Hilbert. The kernel space general equalization fuzzy C-means clustering algorithm is thus obtained. The kernel spaces general equalization fuzzy C-means clustering algorithms can improve the error classification rate of image segmentation by 10% to 30% compared with existing fuzzy compactness and separation (FCS)and fuzzy C-means clustering with local information and kernel metric (KFLICM)algorithms. Experimental results of the clustering analysis of Iris data and gray image segmentation indicate that the proposed general equalization fuzzy C-means clustering algorithm is efficient. Its modified algorithm can obtain more satisfactory clustering quality and segmentation effects than existing fuzzy c-means clustering algorithms. The proposed algorithm overcomes the shortcomings of existing general equalization fuzzy C-means clustering algorithms and improves the clustering performance

which is suitable for complex data analysis.