基于KNN算法的改进K-means算法  

Improving K-means Algorithm Based on KNN Algorithm

作　　者：徐文进[1] 管克航 寻晴晴 许瑶 解钦 XU Wenjin;GUAN Kehang;XUN Qingqing;XU Yao;XIE Qin(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院

出　　处：《青岛科技大学学报（自然科学版）》2019年第5期107-111,118,共6页Journal of Qingdao University of Science and Technology:Natural Science Edition

基　　金：山东省重点研发计划项目(2018GGX105005)

摘　　要：针对于K-means算法的缺点做出了一些改进,提出了一种基于KNN算法改进K-means的算法。改进后的算法解决了K-means算法K值无法确定和数据分类中的不强、易受异常数据干扰的缺点,提高了算法的聚类效果以及削弱初始聚类中心选择的随机性对于聚类结果易陷入局部最优的影响。实验表明,改进后的算法不仅解决了传统算法确定K值的问题,而且聚类结果稳定且聚类效果良好。In this paper,some improvements are made to the shortcomings of K-means algorithm,and an algorithm based on KNN algorithm to improve K-means is proposed.The improved algorithm solves the shortcomings of the K-means algorithm K value can not be determined and the data classification is not strong,susceptible to abnormal data interference,improve the clustering effect of the algorithm and weaken the initial cluster center selection random for the clustering results easy to fall into the local optimal impact.Experiments show that the improved algorithm not only solves the problem of determining the K value by the traditional algorithm,but also has stable clustering results and good clustering effect.

关 键 词：数据中心点 K-MEANS 局部最优 KNN算法 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]