BI-LEVEL CLUSTERING IN TELECOMMUNICATION FRAUD

Luis Pedro Mendes, Joana Dias, Pedro Godinho

2012

Abstract

In this paper we describe a fraud detection clustering algorithm applied to the telecom industry. This is an ongoing work that is being developed in collaboration with a leading telecom operator. The choice of clustering algorithms is justified by the need of identifying clients’ abnormal behaviors through the analysis of huge amounts of data. We propose a novel bi-level clustering methodology, where the first level is concerned with the clustering of transactional data and the second level gathers data from the first phase, along with other information, to build high-level clusters.

References

  1. Barse, E., Kvarnstrom, H., and Jonsson, E. (2003). Synthesizing test data for fraud detection systems. In Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC 2003). Citeseer.
  2. Berkhin, P. (2006). A survey of clustering data mining techniques. Grouping Multidimensional Data, pages 25- 71.
  3. Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
  4. Cortes, C. and Pregibon, D. (2001). Signature-based methods for data streams. Data Mining and Knowledge Discovery, 5(3):167-182.
  5. Cortes, C., Pregibon, D., and Volinsky, C. (2002). Communities of interest. Intelligent Data Analysis, 6(3):211- 219.
  6. Cortesa˜o, L., Martins, F., Rosa, A., and Carvalho, P. (2005). Fraud management systems in telecommunications: a practical approach. In Proceeding of ICT.
  7. Estévez, P., Held, C., and Perez, C. (2006). Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Systems with Applications, 31(2):337-344.
  8. Guha, S., Rastogi, R., and Shim, K. (2000). Rock: A robust clustering algorithm for categorical attributes* 1. Information Systems, 25(5):345-366.
  9. Hand, D. (1997). Construction and assessment of classification rules, volume 15. Wiley.
  10. Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3):283-304.
  11. Krenker, A., Volk, M., Sedlar, U., Better, J., and Kos, A. (2009). Bidirectional Artificial Neural Networks for Mobile-Phone Fraud Detection. Etri Journal, 31(1):92-94.
  12. MacQueen, J. et al. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, pages 281-297.
  13. Moreau, Y., Preneel, B., Burge, P., Shawe-Taylor, J., Stoermann, C., and Cooke, C. (1996). Novel techniques for fraud detection in mobile telecommunication networks. Proceedings of ACTS Mobile Telecommunications Summit, Granada, Spain.
  14. Parsons, L., Haque, E., and Liu, H. (2004). Subspace clustering for high dimensional data: a review. ACM SIGKDD Explorations Newsletter, 6(1):90-105.
  15. Phua, C., Alahakoon, D., and Lee, V. (2004). Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explorations Newsletter, 6(1):50-59.
  16. Takagi, H. (1991). Introduction to fuzzy systems, neural networks, and genetic algorithms. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, pages 405-468.
  17. Viaene, S., Derrig, R., and Dedene, G. (2004). A case study of applying boosting Naive Bayes to claim fraud diagnosis. IEEE Transactions on Knowledge and Data Engineering, 16(5):612-620.
Download


Paper Citation


in Harvard Style

Pedro Mendes L., Dias J. and Godinho P. (2012). BI-LEVEL CLUSTERING IN TELECOMMUNICATION FRAUD . In Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8425-97-3, pages 126-131. DOI: 10.5220/0003718901260131


in Bibtex Style

@conference{icores12,
author={Luis Pedro Mendes and Joana Dias and Pedro Godinho},
title={BI-LEVEL CLUSTERING IN TELECOMMUNICATION FRAUD},
booktitle={Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2012},
pages={126-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003718901260131},
isbn={978-989-8425-97-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - BI-LEVEL CLUSTERING IN TELECOMMUNICATION FRAUD
SN - 978-989-8425-97-3
AU - Pedro Mendes L.
AU - Dias J.
AU - Godinho P.
PY - 2012
SP - 126
EP - 131
DO - 10.5220/0003718901260131