优化初始聚类中心选择的K-means算法

K-Means Algorithm for Optimizing Initial Cluster Center Selection

K-means算法的聚类效果与初始聚类中心的选择以及数据中的孤立点有很大关联,具有很强的不确定性。针对这个缺点,提出了一种优化初始聚类中心选择的K-means算法。该算法考虑数据集的分布情况,将样本点分为孤立点、低密度点和核心点,之后剔除孤立点与低密度点,在核心点中选取初始聚类中心,孤立点不参与聚类过程中各类样本均值的计算。按照距离最近原则将孤立点分配到相应类中完成整个算法。实验结果表明,改进的K-means算法能提高聚类的准确率,减少迭代次数,得到更好的聚类结果。

The clustering effect of K-means algorithm is closely related to the selection of initial clustering center and the isolated points in the data,so it has strong uncertainty.In order to solve this problem,a novel K-means algorithm based on nearest neighbor density is proposed.In this algorithm,considering the distribution of the data set,the sample points are divided into isolated points,low density points and core points,and then the isolated points and low density points are eliminated,and the initial clustering cen⁃ter is selected in the core points.Isolated points do not participate in the calculation of the mean value of all kinds of samples in the process of clustering.The outlier is assigned to the corresponding class according to the nearest principle to complete the whole al⁃gorithm.The experimental results show that the improved K-means algorithm can improve the clustering accuracy,reduce the num⁃ber of iterations,and get better clustering results.