一种改进的K-均值聚类算法

An Improved K-means Clustering Algorithm

为改善传统k均值算法随机选择初始聚类中心导致算法稳定性较差这一问题,提出了一种基于样本空间分布密度的K-均值算法。改进算法将样本分布空间分割为多个大小相同的子空间,通过统计子空间中的样本密度,优化初始聚类中心。实验表明可以有效提高算法稳定性并减少迭代次数,最终获得较好的聚类效果。

A modified K-means clustering algorithm is proposed to improve poor stability of algorithm owing to random selection of the initial clustering centers,which is based on the sample space distribution density.In this paper,the sample space divide into several subspaces of the same size,calculated the sample density in the subspace and determined the first clustering center.Experiments show the proposed can effectively improve the stability,reduce iteration and carry out quite satisfying results.