CPM-Churn Prediction Model for Social Networks

Authors

  • Jai Ganesh V  Computer Science and Engineering, IFET College of Engineerimg, Villupuram, Tamil Nadu, India
  • Akoramurthy B  Computer Science and Engineering, IFET College of Engineerimg, Villupuram, Tamil Nadu, India

Keywords:

Churn prediction model, Social Networks, R.

Abstract

Churn Prediction is very common task in data analytics. It basically consists in trying to predict those customers that are going to quit the contract. In current days, Churn had become the main aspect for social network providers. Based on the history of the customers search patterns and the activities, there is chance to find either they will leave or not. Data mining techniques are found to be more effective in churn prediction to analyze the customer behavior. The comparative study of customer behavioral result in different social networks will predict the churners effectively.

References

  1. Liao, Shu-Hsien, Pei-Hui Chu, and Pei-Yuan Hsiao. "Data mining techniques and applications–A decade review from 2000 to 2011."
  2. N.Hashmi, N. ButtandM.Iqbal. Customer Churn Prediction in Telecommunication A Decade Review and Classification. International Journal of Computer Science Vol.10(5),2013.
  3. V. Umayaparvathi, K. Iyakutti,, "Attribute Selection and Customer Churn Prediction in Telecom Industry", Proceedings of the IEEE International Conference On Data Mining and Advanced Computing, 2016.
  4. V. Umayaparvathi, K. Iyakutti, "Applications of Data Mining Techniques in Telecom Churn Prediction", International Journal of Computer Applications, Vol. 42, No.20, 2012.
  5. "A proposed churn prediction model" IJERA 2, 2012.
  6. M. Mozer, R. H. Wolniewicz, D. B. Grimes, E.Johnson, and H. Kaushansky, "Churn reduction in the wireless industry," in NIPS, 1999, pp. 935–941.
  7. K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, A. A. Nanavati, and A. Joshi, "Social ties and their relevance to churn in Mobile telecom networks," in EDBT ’08, 2008.
  8. H. Hwang, T. Jung, and E. Suh, "An ltv model and customer segmentation based on customer value: a case study on the wireless telecommunication industry," Expert Syst. Appl., vol. 26, no. 2, pp. 181– 188, 2004.
  9. Y. Xie, X. Li, E. W. T. Ngai, and W. Ying, "Customer churn prediction using improved balanced random forests." Expert Syst. Appl., vol. 36, no. 3, pp. 5445–5449, 2009.
  10. B. Q. Huang, M. T. Kechadi, and B. Buckley, "Customer churn prediction for broadband internet services." in DaWaK, ser. Lecture Notes in Computer Science, vol. 5691, 2009, pp. 229–243.
  11. O. Herrera and T. Znati, "Modeling churn in P2P networks," in Annual Simulation Symposium. IEEE Computer Society, 2007, pp. 33–40.
  12. J. Kawale, A. Pal, and J. Srivastava, "Churn Prediction in MMORPGs: A Social Influence Based Approach," in CSE ’09, 2009, pp. 423–428.
  13. S. M. Keaveney, "Customer switching behavior in service industries: An exploratory study," The Journal of Marketing, vol. 59, no. 2, pp. 71–82, 1995.
  14. J. Burez and D. V. den Poel, "Handling class imbalance in customer churn prediction," Expert Syst. Appl, vol. 36, no. 3, pp. 4626–4636, 2009

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Published

2017-04-30

Issue

Section

Research Articles

How to Cite

[1]
Jai Ganesh V, Akoramurthy B, " CPM-Churn Prediction Model for Social Networks, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.394-398, March-April-2017.