计算机与数字工程2018，Vol.46Issue(1)：21-24,113,5.DOI:10.3969/j.issn.1672-9722.2018.01.006
优化初始聚类中心及确定K值的K-means算法
A K-means Algorithm Based on Optimizing the Initial Clustering Center and Determining the K Value
蒋丽 1薛善良1
作者信息
- 1. 南京航空航天大学计算机科学与技术学院 南京 211106
- 折叠
摘要
Abstract
Two parameters in the K-means algorithm need to be input,the one is the number of the K which is needed to clus?tering and the other is the initial clustering center. Selecting the initial cluster centers has a large impact on the clustering results in the algorithm of the K-means,the traditional K-means clustering algorithm selects the clustering center randomly,while randomly select the cluster center will inevitably take the outlier point,this has a large impact on the clustering results. The number of K is in?puted by users,a bad K also has a large impact on the on the clustering results. This paper proposes an improved K-means cluster?ing algorithm that based on the density of the thought ,firstly divides the clustering samples into core point,border point and outlier point,then delete the border point and outlier point from the clustering samples and select the clustering center by using the center of clustering samples,the test shows that the improved algorithm has more stability than before.关键词
K-means聚类/聚类数/聚类中心/密度/孤立点Key words
K-means clustering/clustering number/clustering center/density/outlier point分类
信息技术与安全科学引用本文复制引用
蒋丽,薛善良..优化初始聚类中心及确定K值的K-means算法[J].计算机与数字工程,2018,46(1):21-24,113,5.
