中国邮电高校学报(英文) ›› 2009, Vol. 16 ›› Issue (2): 114-116.doi: 10.1016/S1005-8885(08)60214-9

• Artificial Intelligence • 上一篇    下一篇

Clustering analysis of telecommunication customers

任鸿,郑岩,吴烨蓉   

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-04-30
  • 通讯作者: 任鸿

Clustering analysis of telecommunication customers

REN Hong, ZHENG Yan, WU Ye-rong   

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-04-30
  • Contact: REN Hong

摘要:

In this article, a clustering method based on genetic algorithm (GA) for telecommunication customer subdivision is presented. First, the features of telecommunication customers (such as the calling behavior and consuming behavior) are extracted. Second, the similarities between the multidimensional feature vectors of telecommunication customers are computed and mapped as the distance between samples on a two-dimensional plane. Finally, the distances are adjusted to approximate the similarities gradually by GA. One advantage of this method is the independent distribution of the sample space. The experiments demonstrate the feasibility of the proposed method.

关键词:

genetic;algorithm,;similarity;matrix,;customer;subdivision

Abstract:

In this article, a clustering method based on genetic algorithm (GA) for telecommunication customer subdivision is presented. First, the features of telecommunication customers (such as the calling behavior and consuming behavior) are extracted. Second, the similarities between the multidimensional feature vectors of telecommunication customers are computed and mapped as the distance between samples on a two-dimensional plane. Finally, the distances are adjusted to approximate the similarities gradually by GA. One advantage of this method is the independent distribution of the sample space. The experiments demonstrate the feasibility of the proposed method.

Key words:

genetic algorithm;similarity matrix;customer subdivision