Variable selection by association rules for customer churn prediction of multimedia on demand

Expert Systems with Applications

Volumn 37, Issue 3, 2010, Pages 2006-2015

  Tsai, Chih Fong ^a   Chen, Mao Yuan ^a  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

Association rules; Customer churn prediction; Data mining; Decision trees; Multimedia on demand; Neural networks

Indexed keywords

CUSTOMER CHURN PREDICTION; DATA MINING TECHNIQUES; DATA SETS; EVALUATION MEASURES; F-MEASURE; INTERACTIVE SYSTEM; MOBILE TELECOMMUNICATIONS; MODEL CONSTRUCTION; MODEL PERFORMANCE; MULTIMEDIA ON DEMAND; ONLINE LEARNING; PRE-PROCESSING; PRE-PROCESSING STEP; PREDICTION ACCURACY; PREDICTION MODEL; PREDICTION PERFORMANCE; SOURCE DATA; TELECOMMUNICATION COMPANIES; TV SERVICES; VALUE ADDED SERVICE; VARIABLE SELECTION; WORK FOCUS;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; CUSTOMER SATISFACTION; DATA MINING; DECISION TREES; DISTRIBUTED COMPUTER SYSTEMS; INTERNET; MATHEMATICAL MODELS; MULTIMEDIA SERVICES; NEURAL NETWORKS; SALES; TELECOMMUNICATION INDUSTRY; TELECOMMUNICATION SERVICES; WIRELESS NETWORKS;

MULTIMEDIA SYSTEMS;

EID: 70449521306     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.06.076     Document Type: Article

References (26)

1. Agrawal, R., Imielinski, T., & Swami, A. (1993) Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD conference on management of data (pp. 207-216).
2. Ahn J.-H., Han S.-P., and Lee Y.-S. Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry. Telecommunications Policy 30 10-11 (2006) 552-568
3. Berry M.J.A., and Linoff G. Data mining techniques: For marketing, sales, and customer support (2004), John Wiley & Sons
4. Berson A., Simith S., and Thearling K. Building data mining applications for CRM (2000), McGraw-Hill
5. Buckinx W., and Van den Poel D. Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research 164 1 (2005) 252-268
6. Chiang D.-A., Wang Y.-F., Lee S.-L., and Lin C.-J. Goal-oriented sequential pattern for network banking churn analysis. Expert Systems with Applications 25 3 (2003) 293-302
7. Chu B.-H., Tsai M.-S., and Ho C.-S. Toward a hybrid data mining model for customer retention. Knowledge-Based Systems 20 8 (2007) 703-718
8. Coussement K., and Van den Poel D. Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications 34 1 (2008) 313-327
9. Estévez P.A., Held C.M., and Perez C.A. Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Systems with Applications 31 2 (2006) 337-344
10. Hawkes V.A. The heart of the matter: The challenge of customer lifetime value. CRM Forum Resources (2000) 1-10
11. Haykin S. Neural networks: A comprehensive foundation. 2nd ed. (1999), Prentice Hall
12. Hung C., and Tsai C.-F. Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Applications 34 1 (2008) 780-787
13. Hung S.-Y., Yen D.C., and Wang H.-Y. Applying data mining to telecom churn management. Expert Systems with Applications 31 3 (2006) 515-524
14. Kantardzic M. Data mining - Concepts, models, methods, and algorithms (2003), John Wiley & Sons
15. Kim M., Park M., and Jeong D. The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunications services. Telecommunications Policy 28 (2004) 145-159
16. Luo, B., Shao, P., & Liu, D. (2007) Evaluation of three discrete methods on customer churn model based on neural network and decision tree in PHSS. In The first international symposium on data, privacy, and e-commerce (pp. 95-97).
17. Mitchell T. Machine learning (1997), McGraw Hill
18. Neelakanta P.S., and Preechayasomboon A. Development of a neuroinference engine for ADSL modem applications in telecommunications using an ANN with fast computational ability. Neurocomputing 48 1-4 (2002) 423-441
19. Papagiannidis S., Berry J., and Li F. Well beyond streaming video: IPv6 and the next generation television. Technological Forecasting and Social Change 73 5 (2006) 510-523
20. Qi, J., Zhang, Y., Zhang, Y., & Shi, S. (2006). TreeLogit model for customer churn prediction. In IEEE Asia-Pacific conference on services computing (pp. 70-75).
21. Smith K.A., and Gupta J.N.D. Neural networks in business: Techniques and applications for the operations research. Computers and Operations Research 27 (2000) 1023-1044
22. Sohn S.Y., and Kim Y. Searching customer patterns of mobile service using clustering and quantitative association rule. Expert Systems with Applications 34 2 (2003) 1070-1077
23. Wei C.-P., and Chiu I.-T. Turning telecommunications call details to churn prediction: A data mining approach. Expert Systems with Applications 23 2 (2002) 103-112
24. Weiss G.M. Data mining in telecommunications. In: Maimon O., and Rokach L. (Eds). Data mining and knowledge discovery handbook (2005), Springer
25. Wong B.K., Bodnovich T.A., and Selvi Y. Neural network applications in business: A review and analysis of the literature (1988-1995). Decision Support Systems 19 (1997) 301-320
26. Yang J., and Olafsson S. Optimization-based feature selection with adaptive instance sampling. Computers and Operations Research 33 11 (2006) 3088-3106