Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (6): 1807-1811.DOI: 10.11772/j.issn.1001-9081.2014.06.1807

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Application of biclustering algorithm in high-value telecommunication customer segmentation

LIN Qin1,XUE Yun2   

  1. 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:2014-07-02 Published:2014-06-01
  • Contact: XUE Yun

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. 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
  • 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

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.

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。

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