[1]明勇,张文斌,黄哲学,等.基于RFM购买树的客户分群[J].深圳大学学报理工版,2017,34(3):306-312.[doi:10.3724/SP.J.1249.2017.03306]
 Ming Yong,Zhang Wenbin,Huang Zhexue,et al.Customer segmentation based on RFM purchase tree[J].Journal of Shenzhen University Science and Engineering,2017,34(3):306-312.[doi:10.3724/SP.J.1249.2017.03306]
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基于RFM购买树的客户分群()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第34卷
期数:
2017年第3期
页码:
306-312
栏目:
电子与信息科学
出版日期:
2017-05-30

文章信息/Info

Title:
Customer segmentation based on RFM purchase tree
文章编号:
201703013
作者:
明勇张文斌黄哲学陈小军
深圳大学计算机与软件学院,广东深圳518060
Author(s):
Ming Yong Zhang Wenbin Huang Zhexue and Chen Xiaojun
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
关键词:
计算机感知零售数据客户分群RFM购买树聚类覆盖树Dunn指数
Keywords:
computer perception transaction data customer segmentation recency frequency monetary purchase tree cluster CoverTree Dunn index
分类号:
K 921/927;TP 393
DOI:
10.3724/SP.J.1249.2017.03306
文献标志码:
A
摘要:
针对通过零售交易数据进行客户分群时传统方法未考虑商品的价值问题,提出用RFM(recency frequency monetary)表达交易数据的方法,该方法将客户购买的商品和商品类别组成一棵RFM购买树(recency frequency monetary purchase tree,RFMPT).提出基于RFM购买树的快速聚类算法(based recency frequency monetary purchase tree clustering,BRFMPTC),把购买树构建为CoverTree(CT)索引结构,利用CT结构快速选择k个密度最大的购买树作为中心,将其他对象划分到距它最近的类中心. 实验结果表明,在距离加权下,BRFMPTC算法较传统算法在整体上能产生质量更高的聚类结果,性能得到较大提升.
Abstract:
In order to solve the problem that the value of goods has not been considered in traditional methods of customer segmentation, we propose a method of using the recency frequency monetary purchase tree (RFMPT) to represent transaction data, in which a RFM purchase tree is built based on the category of the goods.Based on the RFM purchase tree,we propose a fast clustering algorithm named based recency frequency monetary purchase tree clustering (BRFMPTC). This algorithm constructs the purchase tree as a CoverTree(CT) index structure. With this structure, we can quickly select the k densest purchase trees as cluster centers, then divide the other objects into the nearest class center.The experimental results show that the performance of the proposed method with distance weighting is better than that of the traditional clustering algorithms.

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备注/Memo

备注/Memo:
Received:2016-11-07;Accepted:2017-02-27
Foundation:National Natural Science Foundation of China (61305059); Natural Science Foundation of Shenzhen University (201432)
Corresponding author:Professor Huang Zhexue.E-mail:zx.huang@szu.edu.cn
Citation:Ming Yong, Zhang Wenbin, Huang Zhexue, et al. Customer segmentation based on RFM purchase tree[J]. Journal of Shenzhen University Science and Engineering, 2017, 34(3): 306-312.(in Chinese)
基金项目:国家自然科学基金资助项目(61305059);深圳大学青年教师科研启动资助项目(201432)
作者简介:明勇(1989—),男,深圳大学硕士研究生.研究方向:数据挖掘.E-mail:13760182207@163.com
引文:明勇,张文斌,黄哲学,等.基于RFM购买树的客户分群[J]. 深圳大学学报理工版,2017,34(3):306-312.
更新日期/Last Update: 2017-04-20