基于密度的K-Means算法及在客户细分中的应用研究

针对K-Means算法所存在的问题进行了深入研究，提出了基于密度的K-Means算法（KMAD算法）。该算法采用聚类对象区域空间的密度分布方法来确定聚类个数K的值，然后用高密度区域的质心作为K-Means算法的初始聚类中心。理论分析与实验结果表明了改进算法的有效性和稳定性，并将改进的算法应用于客户细分研究中。

Research on K-Means clustering algorithm based on density and its application to customer segmentation

The existing problems of K-Means clustering algorithm are carefully researched.An improved K-Means Algorithm based on Density(KMAD) is presented,with which K value of clustering number is located according to the clustering objects distribution density of regional space,and it uses centroids of high -density region as initial clustering center points.Theory analysis and experimental results demonstrate that the improved algorithm can get better clustering than traditional K-Means algorithm at clustering validity and stability,and applied it to customer segmentation.