Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network-Reference-Cited by-同舟云学术

Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network

Published:2018-05-28 Issue:11 Volume:31 Page:7181-7200
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Sivasankar E.,Vijaya J.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-018-3548-4.pdf

Reference44 articles.

1. Mattison R (2001) Telecom churn management: the golden opportunity. APDG Publishing, Fuquay Varina

2. Payne A, Frow P (2005) A strategic framework for customer relationship management. J Mark 69(4):167–176

3. Reinartz W, Krafft M, Hoyer WD (2004) The customer relationship management process: its measurement and impact on performance. J Mark Res 41(3):293–305

4. Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH (2006) Defection detection: measuring and understanding the predictive accuracy of customer churn models. J Mark Res 43(2):204–211

5. Van den Poel D, Lariviere B (2004) Customer attrition analysis for financial services using proportional hazard models. Eur J Oper Res 157(1):196–217

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