优化初始聚类中心选择的K-means算法杨一帆，贺国先，李永定（兰州交通大学交通运输学院，甘肃兰州730070）摘要：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。
关键词：聚类；K-means；最近邻点密度；初始聚类中心；孤立点中图分类号：TP391文献标识码：A文章编号：1009-3044(2021)05-0252-04开放科学（资源服务）标识码（OSID）：K-Means Algorithm for Optimizing Initial Cluster Center SelectionYANG Yi-fan,HE Guo-xian,LI Yong-ding(School of Transportation,Lanzhou Jiaotong University,Lanzhou730070,China)Abstract: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.Key words:clustering;k-means;nearest neighbor density;initial clustering center;isolated points聚类就是按一定的标准把物理或抽象对象的集合分成若干类别的过程，聚类后得到的每一个簇中的对象要尽可能的相似，不同簇中的对象尽量的相异[1-2]。
