Fuzzy C-means clustering based on self-adaptive weight
基于自适应权重的模糊C-均值聚类算法

Due to fuzzy C-means clustering algorithm rely heavily on randomly select C clustering centers,so outlier and uneven distribution of the samples easily influenced and made it easy to fall into the local optimum states.Therefore,this paper proposed an improved fuzzy C-means clustering algorithm based on self-adaptive weights.The new method expressed weight by using the Gaussian distance ratio,it computed the weights for every data according to the current clustering state and no more did rely on the initial clustering center,weakened the influence of outlier and uneven distribution of the samples.The experiments indicate that the fuzzy C-means clustering algorithm based on self-adaptive weights is an effective fuzzy clustering algorithm,has more robust and higher clustering accuracy.