Optimized Kernel Fuzzy C-Means Clustering Algorithm

This paper an optimized kernel fuzzy C-means clustering algorithm (WBAKFCM) is proposed. Firstly, the optimal clustering center is found by the improved bat algorithm (WBA), then the Kernel Fuzzy C-Means clustering algorithm (KFCM) is used to guide the clustering. On the one hand, the improved bat algorithm adding two strategies to the traditional bat algorithm, the good point set theory and velocity weight are used to adjust population initialization and adaptive updates of the individual position respectively. On the other hand, in the Kernel Fuzzy C-Means clustering algorithm, the Gaussian kernel function is selected to map the data to high-dimensional feature space for clustering. The experimental results show that the optimized kernel fuzzy C-means clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency.