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中文摘要：
针对层次聚类法和K-means聚类法的缺陷和不足,提出将二者相结合的改进算法,既解决了层次聚类法伸缩性差的问题,又解决了K-means聚类法对初始聚类中心敏感的问题。通过对改进算法的计算复杂度分析并利用UCI数据库的测试数据对改进算法进行测试。结果表明,混合聚类算法使样本聚类的准确率提高到94%,并有更高的执行效率和更好地实用性。此外,将此算法应用到汽车销售公司的客户细分管理中,得出了差别化明显的客户细分类别,表明此改进算法具有更强的客户细分能力以及客户行为特征的解释能力。
英文摘要：
An improved algorithm is put forward to fuse the hierarchical clustering method and the K- means clustering method to solve both the poor scalability of the former and the sensitivity to the initial clustering center of the latter. The computing complexity analysis of the improved algorithm and the test data of UCI database testing results show that the hybrid clustering algorithm increases the sample clustering accuracy to 94% with a higher efficiency and better practicability. In addition,this algorithm is applied to the car sales company in the management of customer segmentation,where the differential is obtained obviously of customer segmentation categories, showing that the improved algorithm has higher detection rate and stronger interpretation ability on customer behaviors.
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