GSM churn management by using fuzzy c-means clustering and adaptive neuro fuzzy inference system

Expert Systems with Applications

Volumn 38, Issue 3, 2011, Pages 1814-1822

  Karahoca, Adem ^a   Karahoca, Dilek ^a  

^a BAHCESEHIR UNIVERSITY   (Turkey)

Author keywords

ANFIS; Churn management; Data mining; Soft computing; Telecom churn prediction

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS; CHURN MANAGEMENT; CRITICAL ISSUES; CUSTOMER SERVICES; FINANCIAL INFORMATION; FUZZY C MEANS CLUSTERING; FUZZY C-MEANS CLUSTERING ALGORITHMS; GLOBAL SERVICES; GSM OPERATORS; INPUT FEATURES; MOBILE COMMUNICATIONS; NEURO FUZZY CLASSIFIER; POPULATION DATA; PREDICTION MODEL; TELECOM; X-MEANS;

CLASSIFIERS; CLUSTERING ALGORITHMS; DATA MINING; FORECASTING; FUZZY CLUSTERING; FUZZY SYSTEMS; MANAGEMENT; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; POPULATION STATISTICS; SOFT COMPUTING;

FUZZY INFERENCE;

EID: 78049528360     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.07.110     Document Type: Article

References (24)

1. J.C. Bezdek Pattern recognition with fuzzy objective function algoritms 1981 Plenum Press New York
2. S.L. Chiu Extracting fuzzy rules from data for function approximation and pattern classification D. Dubois, H. Prade, R. Yager, Fuzzy information engineering: A guided tour of applications 1997 John Wiley & Sons Chapter 9
3. Cohen, W. (1995). Fast effective rule induction. In Proceedings of the 12th international conference on machine learning, Lake Tahoe, CA (pp. 115-123).
4. J.C. Dunn A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters Journal of Cybernetics 3 1973 32 57
5. Frank, E.; & Witten, I. H. (2005). Data mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann. 0-12-088407-0.
6. Freund, Y.; & Mason, L. (1999). The alternating decision tree learning algorithm. In Proceedings of the sixteenth international conference on machine learning, Bled, Slovenia (pp. 124-133).
7. T.J. Gerpott, W. Rams, and A. Schindler Customer retention loyalty and satisfaction in the German mobile cellular telecommunications market Telecommunications Policy 25 10-11 2001 885 906
8. J. Hadden, A. Tiwari, R. Roy, and D. Ruta Computer assisted customer churn management: State of the art and future trends Journal of Computers and Operations Research 34 10 2005 2902 2917
9. Han, J.; & Kamber, M. (2006). Data mining: Concepts and techniques. San Francisco: Morgan Kaufmann. 978-1-55860-901-3.
10. S-Y. Hung, D.C. Yen, and H.Y. Wang Applying data mining to telecom churn management Journal of Expert Systems with Applications 31 3 2006 515 524
11. J.-S.R. Jang Self-learning fuzzy controllers based on temporal back propagation IEEE Transactions on Neural Networks 3 5 1992 714 723
12. J.-S.R. Jang ANFIS: Adaptive-network-based fuzzy inference system IEEE Transactions on Systems Man and Cybernetics 23 3 1993 665 685
13. J.-S.R. Jang Input selection for ANFIS learning Proceedings of the IEEE International Conference on Fuzzy Systems 1996 1493 1499
14. Karahoca, A.; Karahoca, D.; & Aydin, N. (2007). GSM churn management using an adaptive neuro-fuzzy inference system. In International conference on intelligent pervasive computing (pp. 323-326). Korea: JEJU.
15. E.H. Mamdani, and S. Assilian An experiment in linguistic synthesis with a fuzzy logic controller International Journal of Man-Machine Studies 7 1 1975 1 13
16. M.C. Mozer, R. Wolniewicz, D.B. Grimes, E. Johnson, and H. Kaushanksky Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry IEEE Transactions on Neural Networks 11 3 2000 690 696
17. Pelleg, D.; & Moore, A. (2000). X-means: Extending K-means with efficient estimation of the number of clusters. In Proceedings of the 17th international conference on machine learning (pp. 727-734). Morgan Kaufmann.
18. M. Sugeno, and G.T. Kang Sturcture identification of fuzzy model Fuzzy Sets and Systems 28 1988 15 33
19. T. Takagi, and M. Sugeno Fuzzy identification of systems and its application to modeling and control IEEE Transactions on Systems Man and Cybernetics 15 1985 116 132
20. Y. Tsukamato An approach to fuzzy reasoning method M.M. Gupta, R.K. Ragade, R.R. Yager, Advances in fuzzy set theory and applications 1979 North-Holland Amsterdam 137 149
21. C.P. Wei, and I-T. Chiu Turning telecommunications call details to churn prediction: A data mining approach Journal of Expert Systems with Applications 23 2002 103 112
22. Werbos, P. (1974). Beyond regression, new tools for prediction and analysis in the behavioural sciences. Unpublished PhD Thesis. Harvard University.
23. Yan, L.; Fassion, M.; & Baldasare, P. (2005). Predicting customer behavior via calling links. In Proceedings of international joint conference on neural networks (pp. 2555-2560), Montreal, Canda.
24. Yu, W.; Jutla, D. N.; & Sivakumar, S. C. (2005). A churn management alignment model for managers in mobile telecom. In Proceedings of the 3rd annual communication networks and services research conferences (pp. 48-53).