计算机工程 ›› 2008, Vol. 34 ›› Issue (24): 37-39.doi: 10.3969/j.issn.1000-3428.2008.24.013

• 软件技术与数据库 • 上一篇    下一篇

聚类算法在银行客户细分中的应用

花海洋,赵怀慈   

  1. (中国科学院沈阳自动化研究所,沈阳 110016)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-12-20 发布日期:2008-12-20

Application of Clustering Algorithms in Bank Customer Segmentation

HUA Hai-yang, ZHAO Huai-ci   

  1. (Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-12-20 Published:2008-12-20

摘要: 针对聚类算法在金融领域广泛应用的实际情况,基于银行客户数据集,对DBSCAN, K-means和X-means 3种聚类算法在执行效率、可扩展性、异常点检测能力等方面进行对比分析,并提出将X-means算法应用于银行业客户细分。利用X-means算法建立了一套银行客户细分模型,为银行决策者提供科学的决策支持。

关键词: 聚类, K-means算法, X-means算法, 客户细分

Abstract: This paper considers cluster analysis, which is the most often applied in commerce area and discusses three algorithms: DBSCAN, K-means and X-means based on the bank customer standard dataset. It compares algorithms concerning their effectiveness, scalability, outliers detection ability, and builds some bank customer models with X-means, which provides more powerful suggestions for bank decision-making man.

Key words: clustering, K-means algorithm, X-means algorithm, customer segmentation

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