Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise.

高斯核是由欧几里得距离定义的，已被广泛用于许多领域。鉴于欧几里得距离对异常值或噪声敏感，因此对于复杂的非凸数据很难获得令人满意的结果。首先将Jenson-Shannon / Bregman发散与凸函数相结合来诱发类似熵的发散，并构造其基于象熵的发散核的商业核函数。其次，通过改进基于核的模糊局部信息C-均值聚类（KWFLICM）的折衷权重因子，提出了一种基于基于熵的散度核的模糊加权因子。最后，提出了基于基于熵的散度核（EKWFLICM）的加权模糊局部信息聚类，它结合了新的加权模糊因子和基于熵的散度核。实验结果表明，该算法在存在高噪声的情况下，优于现有的模糊聚类相关算法的分割性能。