双聚类算法在电信高价值客户细分的应用

doi:10.11772/j.issn.1001-9081.2014.06.1807

计算机应用 ›› 2014, Vol. 34 ›› Issue (6): 1807-1811.DOI: 10.11772/j.issn.1001-9081.2014.06.1807

双聚类算法在电信高价值客户细分的应用

林勤¹,薛云²

1. 广东医学院信息工程学院,广东东莞 523808;
2. 华南师范大学物理与电信工程学院,广州 510006

收稿日期:2013-11-22 修回日期:2014-01-03 出版日期:2014-06-01 发布日期:2014-07-02
通讯作者: 薛云
作者简介:林勤(1987-),男,广东揭阳人,助理实验师,硕士研究生,主要研究方向:数据挖掘、并行计算、生物信息学;薛云(1975-),男,湖南岳阳人,副教授,博士,主要研究方向：数据挖掘、模式识别。
基金资助:
国家自然科学基金资助项目;广州市科技计划项目;广东医学院面上基金资助项目

Application of biclustering algorithm in high-value telecommunication customer segmentation

LIN Qin¹,XUE Yun²

1. School of Information Engineering, Guangdong Medical College, Dongguan Guangdong 523808, China;
2. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou Guangdong 510006, China

Received:2013-11-22 Revised:2014-01-03 Online:2014-06-01 Published:2014-07-02
Contact: XUE Yun

摘要/Abstract

摘要：

针对传统客户价值细分方法在高价值客户细分时不够精细化的问题,引入了大均值子矩阵(LAS)双聚类算法。该方法在客户样本和消费属性两个维度上对消费记录进行双向聚类,可以挖掘出高消费、高价值的客户群体。以某电信公司的高价值客户细分为实例,通过定义一个价值尺度和构建一个PA指标,将所提算法与K均值(K-means)算法进行性能比较,实验结果表明,所提算法能挖掘出更多的高价值客户群体,且能够对客户属性进行更加精细的划分,因此它更适合应用于高价值客户市场的识别和细分。

Abstract:

To improve the accuracy of traditional method for customer segmentation, the Large Average Submatrix (LAS) biclustering algorithm was used, which performed clusting on customer samples and consumer attributes simultaneously to identify the upscale and high-value customers. By introducing a new value yardstick and a novel index named PA, the LAS biclustering algorithm was compared with K-means clustering algorithm based on a simulation experiment on consumption data of a telecom corporation. The experimental result shows that the LAS biclustering algorithm finds more groups of high-value customers and obtains more accurate clusters. Therefore, it is more suitable for recognition and segmentation of high-value customers.

中图分类号:

TP391

林勤薛云. 双聚类算法在电信高价值客户细分的应用[J]. 计算机应用, 2014, 34(6): 1807-1811.

LIN Qin XUE Yun. Application of biclustering algorithm in high-value telecommunication customer segmentation[J]. Journal of Computer Applications, 2014, 34(6): 1807-1811.

参考文献

[1]ZEITHAML V A, RUST R T, LEMON K N. The customer pyramid: creating and serving profitable customers [J]. California Management Review, 2001,43(4):118-142.

[2]JACKSON B B. Build customer relationships that last [J]. Harvard Business Review, 1985,63(10):120-128.

[3]BERGER P D, NASR N I. Customer lifetime value: marketing models and applications [J]. Journal of Interactive Marketing, 1998,12(1):17-30.

[4]CHEN M. Research of customer retention and lifetime value [D]. Xi'an: Xi'an Jiaotong University, 2001.(陈明亮.客户保持与生命周期研究 [D].西安:西安交通大学,2001.)

[5]QI J. Research of enterprise customer value [D]. Xi'an: Xi'an Jiaotong University, 2002.(齐佳音.企业客户价值研究[D].西安:西安交通大学,2002.)

[6]QU Z, ZHENG Y, LYU T. Realizing customer behavious analysis based on clustering [J]. Journal of Northeast Normal University: Natural Science, 2006,38(2):19-21.(曲昭伟,郑岩,吕廷杰.基于聚类实现客户行为分析[J].东北师大学报:自然科学版,2006,38(2):19-21.)

[7]ZHAO M, NI Z, LIU B. Application research of K-means clustering and naive Bayesian algorithm in business intelligence [J]. Computer Technology and Development, 2010,20(4):179-182.(赵敏, 倪志伟, 刘斌.K-means与朴素贝叶斯在商务智能中的应用[J].计算机技术与发展,2010,20(4):179-182.)

[8]ZHENG G, ZHANG B, GUO P, et al. Analysis of clustering algorithm in behavior mode of customers in China telecom [J]. Journal of Chongqing University: Natural Science, 2006,29(4):119-121.(郑国荣,张邦礼,郭鹏,等.聚类分析在电信消费模式中的应用[J].重庆大学学报:自然科学版,2006,29(4):119-121.)

[9]SHABALIN A A, WEIGMAN V J, PEROU C M, et al. Finding large average submatrices in high dimensional data [J]. The Annals of Applied Stastistics, 2009,3(3):985-1012.

[10]CHENG Y, CHURCH G M. Biclustering of expression data [EB/OL]. [2013-07-03]. ftp://samba.ad.sdsc.edu/pub/sdsc/biology/ISMB00/157.pdf.

[11]DHILLON I S. Co-clustering documents and words using bipartite spectral graph partitioning [C]// KDD 2001: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2001:269-274.

[12]BANERJEE A, DHILLON L, GHOSH J, et al. A generalized maximum entropy approach to Bregman co-clustering and matrix approximations [C]// KDD 2004: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2004:509-514.

[13]SU X, KHOSHGOFTAAR T M. A survey of collaborative filtering techniques [J]. Advances in Artificial Intelligence, 2009,2009(4):421-445.ns [J]. Journal of Cybernetica, 1974,4(1):95-104.

[15]CALINSKI T, HARABASZ J. A dendrite method for cluster analysis [J]. Communication in Stastistics, 1974,3(1):1-27.

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双聚类算法在电信高价值客户细分的应用

Application of biclustering algorithm in high-value telecommunication customer segmentation

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