Data Mining in Telecommunications

Telecommunication companies generate a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which decsribes the telecommmunication customers. This chapter describes how Data Mining can be used to uncover useful information buried within these data sets. Several Data Mining applications are described and together they demonstrate that Data Mining can be used to identify telecommunication fraud, improve marketing effectiveness, and identify network faults.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Buy Now

eBook EUR 179.99 Price includes VAT (France)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Preview

References

• Cortes, C., Pregibon, D. Signature-based methods for data streams. Data Mining and Knowledge Discovery 2001; 5(3): 167–182. ArticleGoogle Scholar
• Cortes, C., Pregibon, D. Giga-mining. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining; 174–178, 1998 August 27–31; New York, NY: AAAI Press, 1998. Google Scholar
• Ezawa, K., Norton, S. Knowledge discovery in telecommunication services data using Bayesian network models. Proceedings of the First International Conference on Knowledge Discovery and Data Mining; 1995 August 20–21. Montreal Canada. AAAI Press: Menlo Park, CA, 1995. Google Scholar
• Fawcett, T., Provost, F. Adaptive fraud detection. Data Mining and Knowledge Discovery 1997; 1(3):29l–316. ArticleGoogle Scholar
• Fawcett, T, Provost, F. Activity monitoring: Noticing interesting changes in behavior. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 53–62. San Diego. ACM Press: New York, NY, 1999. Google Scholar
• Han, J., Altman, R. B., Kumar, V., Mannila, H., Pregibon, D. Emerging scientific applications in Data Mining. Communications of the ACM 2002; 45(8): 54–58 ArticleGoogle Scholar
• Kaplan, H., Strauss, M., Szegedy, M. Just the fax—differentiating voice and fax phone lines using call billing data. Proceedings of the Tenth Annual ACM-SIAM Symposium on Discrete Algorithms. 935–936. Baltimore. Socieiy for Industrial and Applied Mathematics: Philadelphia, PA, 1999. Google Scholar
• Klemettinen, M., Mannila, H., Toivonen, H. Rule discovery in telecommunication alarm data. Journal of Network and Systems Management 1999; 7(4):395–423. ArticleGoogle Scholar
• Mani, D. R., Drew, J., Betz, A., Datta, P. Statistics and data mining techniques for lifetime value modeling. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 94–103. San Diego. ACM Press: New York, NY, 1999. Google Scholar
• Roset, S., Murad, U., Neumann, E., Idan, Y., Pinkas, G. Discovery of fraud rules for telecommunications—challenges and solutions. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 409–413, San Diego CA. New York: ACM Press, 1999. Google Scholar
• Sasisekharan, R., Seshadri, V., Weiss, S. Data Mining and forecasting in largescale telecommunication networks. IEEE Expert 1996; 11(1):37–43. ArticleGoogle Scholar
• Weiss, G. M., Hirsh, H. Learning to predict rare events in event sequences. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining. 359–363. AAAI Press, 1998. Google Scholar
• Weiss, G. M., Provost, F. Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 2003; 19:315–354. Google Scholar
• Weiss, G. M., Ros, J, Singhal, A. ANSWER: Network monitoring using object-oriented rule. Proceedings of the Tenth Conference on Innovative Applications of Artificial Intelligence; 1087–1093. AAAI Press, Menlo Park, CA, 1998. Google Scholar

Author information

Authors and Affiliations

1. Department of Computer and Information Science, Fordham University, 441 East Fordham Road, Bronx, NY, 1O458 Gary M. Weiss

1. Gary M. Weiss

Editor information

Editors and Affiliations

1. Dept. of Industrial Engineering, Tel-Aviv University, 69978, Ramat-Aviv, Israel Oded Maimon & Lior Rokach &

Rights and permissions

Copyright information

© 2005 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

Weiss, G.M. (2005). Data Mining in Telecommunications. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/0-387-25465-X_56

Download citation

• .RIS
• .ENW
• .BIB

• DOI : https://doi.org/10.1007/0-387-25465-X_56
• Publisher Name : Springer, Boston, MA
• Print ISBN : 978-0-387-24435-8
• Online ISBN : 978-0-387-25465-4
• eBook Packages : Computer ScienceComputer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Get shareable link

Sorry, a shareable link is not currently available for this article.

Copy to clipboard

Provided by the Springer Nature SharedIt content-sharing initiative