应用因子分析和K-MEANS聚类的客户分群建模  

Customer Segmentation Modeling on Factor Analysis and K-MEANS Clustering

作　　者：彭凯[1] 秦永彬[1] 许道云[1] 

机构地区：[1]贵州大学计算机科学与信息学院,贵阳550025

出　　处：《计算机科学》2011年第5期154-158,198,共6页Computer Science

基　　金：国家自然科学基金项目(60863005;61011130038);贵州省省长基金(200404)资助

摘　　要：为挖掘存量用户的潜在数据业务使用需求,研究客户细分成为各电信运营商进行差异化营销所必须解决的问题。利用聚类算法提出了一种解决电信短信业务客户分群的应用模型。首先基于因子分析为复杂参数变量下的数据挖掘有效地减少了冗余字段,提高了模型构建的质量和效率,然后通过无监督的K-MEANS分群算法完成分群。经验证,该短信分群模型具备明显的特征差异性。2009年某西部通信企业应用该模型在数据业务差异化营销中取得了明显的效益。To develop customers＇ potential demands for data services,the research for customer segmentation has become a primitive work of telecommunications operators in order to run a differentiated users＇ marketing.Through the use of clustering algorithm,this paper presented a segmentation modeling for differentiating customers using short messaging services in telecommunications operators.Firstly,based on factor analysis,redundant properties were simplified in the complex data mining under variable parameters in order to improve the quality and efficiency of the modeling,and then the customer segmentation model was constructed through unsupervised clustering K-MEANS algorithm.It was verified that the SMS users have the obvious differentiation of characteristics by using the cluster model.In 2009,a western communications enterprise achieved significant benefits with application of the model in the differentiated data service marketing.

关 键 词：增值业务 因子分析 短信渗透率 数据探索 数据训练 时间窗口 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]