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Received:September 07, 2009 Revised:October 21, 2009
Received:September 07, 2009 Revised:October 21, 2009
中文摘要: 首先比较了DBSCAN，CLIQUE，CLARANS，K-means 和 X-means等聚类算法，接着选用X-means聚类算法建立了金融产品客户细分模型，然后结合关联强度分析，设计了支持交叉营销的金融产品客户数据挖掘系统，并给出了一个系统使用示例。
中文关键词: 交叉营销 X-means聚类算法 客户细分 金融产品营销
Abstract:This paper makes a comparison between Clustering algorithms such as DBSCAN, CLIQUE, CLARANS, K-means and X-means. The X-means clustering algorithm is selected to establish a customer segmentation model for financial products marketing. Based on relational analysis of financial products, a financial products customer data mining application system is designed to support the cross-marketing of financial products. In the end, a use case is given to illustrate the application of the system.
keywords: cross-marketing X-means clustering algorithm customer segmentation financial product marketing
文章编号： 中图分类号： 文献标志码：
基金项目:
| Author Name | Affiliation |
| HUANG Hong | 浙江工业大学 软件学院 浙江 杭州 310023 |
| HONG Yi |
| Author Name | Affiliation |
| HUANG Hong | 浙江工业大学 软件学院 浙江 杭州 310023 |
| HONG Yi |
引用文本：
黄洪,洪毅.支持交叉营销的金融产品客户数据挖掘.计算机系统应用,2010,19(5):100-103
HUANG Hong,HONG Yi.Customer Data Mining for Supporting Cross-Marketing of Financial Products.COMPUTER SYSTEMS APPLICATIONS,2010,19(5):100-103
黄洪,洪毅.支持交叉营销的金融产品客户数据挖掘.计算机系统应用,2010,19(5):100-103
HUANG Hong,HONG Yi.Customer Data Mining for Supporting Cross-Marketing of Financial Products.COMPUTER SYSTEMS APPLICATIONS,2010,19(5):100-103
