Kernel Parameter Optimization in Stretched Kernel-Based Fuzzy Clustering

Although the kernel-based fuzzy c-means (KFCM) algorithm utilizing a kernel-based distance measure between patterns and cluster prototypes outperforms the standard fuzzy c-means clustering for some complex distributed data, it is quite sensitive to selected kernel parameters. In this paper, we propose the stretched kernel-based fuzzy clustering method with optimized kernel parameter. The kernel parameters are updated in accordance with the gradient method to further optimize the objective function during each iteration process. To solve the local minima problem of the objective function, a function stretching technique is applied to detect the global minimum. Experiments on both synthetic and real-world datasets show that the stretched KFCM algorithm with optimized kernel parameters has better performance than other algorithms.