A Computational Study of Replicated Clustering with an Application to Market Segmentation*
通过蒙特卡洛模拟，研究了在k均值聚类中采用基于层次聚类的种子选择方法进行多次复制的优势，并给出了真实市场细分应用案例。
ABSTRACT In most commercial applications of k ‐means clustering, researchers choose one set of k seed points to start the partitioning process; often, the initial set of seeds is chosen randomly. Using Monte Carlo simulation, we show that significant benefits are associated with replicated starting configurations that incorporate seed selection procedures based on a hierarchical clustering of sample points drawn from the original data matrix. A real‐world application of the approach is then presented.