[1]刘芝怡,陈 功.基于改进K-means算法的RFAT客户细分研究[J].南京理工大学学报(自然科学版),2014,38(04):531-536.
 Liu Zhiyi,Chen Gong.RFAT customer segmentation based on improved K-means algorithm[J].Journal of Nanjing University of Science and Technology,2014,38(04):531-536.
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基于改进K-means算法的RFAT客户细分研究
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
38卷
期数:
2014年04期
页码:
531-536
栏目:
出版日期:
2014-08-31

文章信息/Info

Title:
RFAT customer segmentation based on improved K-means algorithm
作者:
刘芝怡1陈 功2
常州工学院 1.计算机信息工程学院; 2.电子信息与电气工程学院,江苏 常州 213002
Author(s):
Liu Zhiyi1Chen Gong2
1.Department of Computer Science and Information Engineering; 2.Department of Electronic Information and Electrical Engineering,Changzhou Institute of Technology,Changzhou,213002,China
关键词:
客户分类 购买时间 购买频次 平均购买额 购买倾向 K-means算法 初始聚类中心 聚类数
Keywords:
customer classification recency frequency average monetary trentd K-means algorithm initial clustering centers number of clusters
分类号:
TP39
摘要:
为了解决传统K-means算法对初始聚类中心敏感和聚类数目事先难以确定的问题,提出了一种改进的K-means算法。改进算法利用最大距离等分策略来选取初始聚类中心,并利用一种评价函数来自动确定聚类数,减少了算法结果对参数的依赖。将改进算法应用到某企业客户分类中时,为提高分类结果的表征性,提出了以客户最近购买时间(Recency)、 购买频次(Frequency)、平均购买额(Average Monetary)和购买倾向(Trend)作为客户价值细分变量的RFAT(Recency,frequency,average monetary and trend)模型,对客户RFAT值进行了聚类分析,并提供了针对不同客户群的营销策略。实证研究表明,该文所提出的改进算法和模型可以有效地对企业客户进行分类,能充分反映客户的当前价值和增值潜能。
Abstract:
The traditional K-means algorithm has sensitivity to the initial cluster centers,meanwhile it is difficult for users to determine the optimal number of clusters in advance.In order to solve these problems,a new improved K-means algorithm is proposed here.The algorithm can optimize the initial center points through computing the maximum distance of objects.At the same time,it can find the optimal number of clusters by using a new evaluation function.The results can reduce the dependence on the parameters.When the improved algorithm is used to analyze customers of a firm,the RFAT customer classification model is proposed.The new model has four segmentation variables to assess the customer's value:Recency,Frequency,Average Monetary and Trend.The customers RFAT-value is analyzed by using clustering.The business strategy for different customer groups is also pointed out.The application results show that the RFAT model and the improved K-means algorithm proposed here can classify customers effectively.It also can fully reflect the customer's current value and appreciation potential.

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备注/Memo

备注/Memo:
收稿日期:2013-12-29 修回日期:2014-05-13
基金项目:江苏省自然科学基金(BK20130245)
作者简介:刘芝怡(1977-),女,讲师,主要研究方向:数据挖掘,E-mail:liuzy@czu.cn。
引文格式:刘芝怡,陈功.基于改进K-means算法的RFAT客户细分研究[J].南京理工大学学报,2014,38(4):531-536.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2014-08-31