文献类型：期刊文献

英文题名：Method of the Kernel-based Maximum Entropy Fuzzy C-means Clustering

作者：Chen, Weiye[1]; Zhu, Kunping[1]

机构：[1] East China University of Science and Technology, School of Mathematics, Shanghai, China

年份：2023

起止页码：577

外文期刊名：2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning, PRML 2023

收录：EI(收录号：20240315378216)

语种：英文

外文关键词：Fuzzy systems - K-means clustering - Maximum entropy methods

摘要：Most studies incorporating entropy into the fuzzy c-means clustering (FCM) often overlook the fuzzy coefficient, while considering this coefficient typically involves complex iterative derivation and computation. In this paper, a method of the kernel-based maximum entropy fuzzy c-means clustering (K-MEFCM) is proposed. By preserving the fuzzy coefficient, information entropy is added as a regularization term to the objective function, leading to more balanced results. Moreover, to enhance the algorithm's stability in noisy environments and its ability to handle non-linear and complex data, the Euclidean distance is replaced with a distance induced by the Gaussian kernel function. The derivation process innovatively utilizes the Lambert function to obtain explicit solutions and derive concise iterative formulas, avoiding the complexity of approximate solutions. Additionally, the k-means++ initialization method is adopted to reduce the influence of randomization. Experimental results validate the excellent performance of the proposed method. ? 2023 IEEE.