计算机应用研究2017，Vol.34Issue(6)：1617-1620,4.DOI:10.3969/j.issn.1001-3695.2017.06.004
最优聚类个数和初始聚类中心点选取算法研究
Algorithm research of optimal cluster number and initial cluster center
张素洁 1赵怀慈2
作者信息
- 1. 中国科学院沈阳自动化研究所,沈阳110016
- 2. 中国科学院大学,北京100049
- 折叠
摘要
Abstract
The cluster k of traditional K-means algorithm could not determine beforehand and the initial clustering centers of K-means algorithm were randomly selected,which might result in low accurary and unstable clustering.This paper based on the SSE for selecting the number of clusters k,based on the principle that the clustering center of the surrounding area was relatively dense,and between the clustering center distance was relatively far,selected the initial clustering center to avoid the initial clustering center focused on a small range,prevented fall into local optimum.In the case of the number of categories k was given.This paper used the standard UCI data sets for test.Tests show that,this method can select the optimal value of k,it can choose the only center of initial clustering and have the higher accuracy and the minimum errors.关键词
K-means算法/聚类中心/准确率/误差平方和Key words
K-means algorithm/cluster centers/accuracy/squared error分类
信息技术与安全科学引用本文复制引用
张素洁,赵怀慈..最优聚类个数和初始聚类中心点选取算法研究[J].计算机应用研究,2017,34(6):1617-1620,4.
