最优聚类个数和初始聚类中心点选取算法研究

摘要

传统K-means算法的聚类数k值事先无法确定,而且算法是随机性地选取初始聚类中心点,这样容易造成聚类结果不稳定且准确率较低.基于SSE来选取聚类个数k值,基于聚类中心点所在的周围区域相对比较密集、聚类中心点之间距离相对较远的选取原则来选取初始聚类中心点,避免初始聚类中心点集中在一个小的范围,防止陷入局部最优.实验证明,该算法能选取最优的k值,通过用标准的UCI数据库进行实验,采用的算法能选择出唯一的初始中心点,聚类准确率较高、误差平方和较小.%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.