Mercer kernel based fuzzy clustering self-adaptive algorithm

A novel mercer kernel based fuzzy clustering self-adaptive algorithm was presented. The mercer kernel method was introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters was first determined. A self-adaptive algorithm was proposed. The number of clusters, which was not given in advance, could be gotten automatically by a validity measure function. Finally, experiments were given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.