A Kernel-Based And Sample-Weighted Fuzzy Clustering Algorithm

Among the clustering algorithms based on objective functions, Fuzzy c-means algorithm is the most perfect and widely used. It is an important way of data analysis, but the algorithm is greatly influenced by the outliers and it is not good for nonlinear data. To overcome these shortcomings, a new clustering algorithm is proposed which is based on sample weighting and applying the kernel function. It can implicitly perform the data mapping into a high dimensional feature space. In this way, the data is more clearly separable, and the outliers can be filtered greatly. Through the simulation, the proposed method is characterized by higher clustering accuracy than the previous algorithms.