资 源 简 介
基于改进K-means的压缩IP
由于k-means本身受异常点影响较大，这里采用迭代k-means的方法，降低异常点的影响，减少计算量和提高聚类数目的灵活性。并添加合并异常聚类方法，提高聚类的均匀性-K-means based on improved compression IP
As k-means itself is influenced by outliers, where an iterative k-means method to reduce the impact of outliers, reducing the number of cluster computing capacity and enhanced flexibility. And add the combined abnormal clustering method to improve the uniformity of clustering
