An Optimized k_means Algorithm

The traditional k_means randomly selects initial cluster centers and divides the sample points by Euclidean distance in the original space, so the accuracy classification isnt't enough. The min_max algorithm obtatins more improvement than the traditional algorithm.However,the traditional algorithm and the min_max algorithm neglect the irrationality of the classification by Euclidean distance.The improved algorithm maps the input vectors into the feature space and determines the initial centers,at last clusters by the distance between two points in the feature space. The improved algorithm is evaluated on available datasets called iris and wine.