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Customer Churn Prediction of China Telecom Based on Cluster Analysis and Decision Tree Algorithm

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Emerging Research in Artificial Intelligence and Computational Intelligence (AICI 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 315))

Abstract

In this paper, cluster analysis is applied in the customer data provided by a branch of China Unicom in Guangdong province. It is established that customer churn prediction model of China Telecom based on cluster analysis and decision tree algorithm. The prediction model can provide scientific basis and reference for maintaining and retaining customers of China Telecom, since it can efficiently discover the valuable customers with leaving orientation from massive information of customers. The experimental results show that the customer churn prediction model in this paper is effective.

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Author information

Authors and Affiliations

  1. School of Applied Mathematics, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China

    Guangqing Li & Xiuqin Deng

Authors
  1. Guangqing Li
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  2. Xiuqin Deng
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Editor information

Editors and Affiliations

  1. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  2. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

  3. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, Victoria, Australia

    Hepu Deng

  4. School of Electronics and Information, Tongji University, China, 201804, Shanghai, China

    Duoqian Miao

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, G., Deng, X. (2012). Customer Churn Prediction of China Telecom Based on Cluster Analysis and Decision Tree Algorithm. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Emerging Research in Artificial Intelligence and Computational Intelligence. AICI 2012. Communications in Computer and Information Science, vol 315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34240-0_42

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  • DOI: https://doi.org/10.1007/978-3-642-34240-0_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34239-4

  • Online ISBN: 978-3-642-34240-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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