摘要
在数据仓库建设基本到位之后,银行应如何对庞大的客户信息进行深层次数据挖掘,建立客户与市场的细分体系,从而在经营与管理中发挥作用,是一个极为重要与紧迫的研究课题。在介绍客户细分理论与数据挖掘技术的基础上,对银行客户细分形式化描述过程模型做了讨论,并以K均值聚类算法对银行实证客户数据进行了挖掘,实验表明数据挖掘技术在银行客户细分方面的应用具有一定的有效性。
After the data warehouse built into place,it becomes an extremely important and urgent issue for banks on how to conduct deep-level data mining at a mass of customer information,and how to establish customer and market segmentation systems so as to play its role in banking business and management.This paper outlines the theory of customer segmentation and data-mining technologies.Then it gives a formal description of the bank customer segmentation process model and has a positive customers data mining experiment by using K-means clustering algorithm.The experiment indicates that application of data mining technologies in bank customer segmentation is efficient.
出处
《金华职业技术学院学报》
2007年第4期44-47,39,共5页
Journal of Jinhua Polytechnic
关键词
数据挖掘
数据仓库
银行
客户细分
data mining
data warehouse
bank
customer segmentation
