K-means算法的初始聚类中心的优化

Optimization study on initial center of K-means algorithm

传统的K-means算法对初始聚类中心敏感,聚类结果随不同的初始输入而波动,针对K-means算法存在的问题,提出了基于密度的改进的K-means算法,该算法采取聚类对象分布密度方法来确定初始聚类中心,选择相互距离最远的K个处于高密度区域的点作为初始聚类中心,理论分析与实验结果表明,改进的算法能取得更好的聚类结果。

The traditional K-means algorithm has sensitivity to the initial centers.To solve this problem,an improved K-means algorithm based on density is presente.First it computes the density of the area where the data object belongs to;then finds K data objects all of which are belong to high density area and the most far away to each other,using these K data objects as the initial start centers.Theory analysis and experimental results demonstrate that the improved algorithm can get better clustering .and eliminate the sensitivity to the initial start centers.