A novel method based on snake optimization algorithm for improving fuzzy clustering

Abstract

In order to effectively solve the instability of clustering results caused by random initial clustering centers of fuzzy clustering, a new method of fuzzy clustering based on improved snake optimization algorithm is proposed. Firstly, the population initialization of the snake optimization algorithm is improved; then the improved algorithm is applied to preprocess the dataset to obtain the initial clustering centers based on the dataset; finally, the generative clustering centers are used to carry out the iterative updating of fuzzy clustering. By comparing with the traditional fuzzy clustering algorithm, the results show that the initial clustering center generated based on the improved snake optimization algorithm can effectively avoid falling into the local optimal solution and has better robustness, which can effectively improve the stability and accuracy of the clustering algorithm.