Weighted K-means clustering algorithm based on Fisher's linear discriminant ratio

To improve the clustering effect of K-means,the features were successively weighted by using the method of Fisher's linear discriminant ratio according to their contribution in clustering and the data were clustered. The experimental results done on synthetic and real data show that the method has superiority over other weighted K-means clustering algorithm in the clustering effect.