文献类型：期刊文献

英文题名：Improved Kernel-based Fuzzy Clustering Algorithm Based on Maximum Tsallis Entropy

作者：Fang, Zheng[1]; Zhu, Kunping[2]

机构：[1] East China University of Science and Technology, School of Mathematics, Shanghai, China; [2] East China University of Science and Technology, Dept. of Mathematics, Shanghai, China

年份：2023

外文期刊名：ICNC-FSKD 2023 - 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

收录：EI(收录号：20234515025216)

语种：英文

外文关键词：Clustering algorithms - Iterative methods

摘要：This paper proposes an improved kernel-based fuzzy clustering algorithm based on maximum Tsallis entropy (MTE-IKFC). The algorithm is on the basis of the kernel-based fuzzy clustering algorithm to reduce the influence of data that doesn't belong to the current cluster on the iterative solution of the corresponding clustering center, and suppress the convergence of different clustering centers when the regularization coefficient is small. In order to effectively reduce the influence of differences in sample distribution on the solution of clustering center, the weight based on relative density is introduced to make the clustering results more reasonable. Finally, Shannon entropy is extended to Tsallis entropy with a generalization parameter q, which further improves the performance of the algorithm. In the experimental part, compared with the other four clustering algorithms, the proposed algorithm shows superior clustering performance and the best robustness. ? 2023 IEEE.