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Credit-Card Fraud Profiling Using a Hybrid Incremental Clustering Methodology

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Book cover Scalable Uncertainty Management (SUM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7520))

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Abstract

This paper addresses the task of helping investigators identify characteristics in credit-card frauds, so as to establish fraud profiles. To do this, a clustering methodology based on the combination of an incremental variant of the linearised fuzzy c-medoids and a hierarchical clustering is proposed. This algorithm can process very large sets of heterogeneous data, i.e. described by both categorical and numeric features. The relevance of the proposed approach is illustrated on a real dataset containing next to one million fraudulent transactions.

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References

  1. Banque de France: Annual Report of the Observatory for Payment Card Security (2010), http://www.banque-france.fr/observatoire/telechar/gb/2010/rapport-annuel-OSCP-2010-gb.pdf

  2. Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Statistical Science 17, 235–255 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Phua, C., Lee, V., Smith, K., Gayler, R.: A comprehensive survey of data mining-based fraud detection research. Artificial Intelligence Review (2005)

    Google Scholar 

  4. Laleh, N., Azgomi, M.A.: A taxonomy of frauds and fraud detection techniques. Information Systems, Technology and Management Communications in Computer and Information Science 3, 256–267 (2009)

    Article  Google Scholar 

  5. Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems 9, 595–607 (2001)

    Article  Google Scholar 

  6. Cheng, T.W., Goldgof, D., Hall, L.: Fast fuzzy clustering. Fuzzy Sets and Systems 93, 49–56 (1998)

    Article  MATH  Google Scholar 

  7. Altman, D.: Efficient fuzzy clustering of multi-spectral images. In: Proc. of the IEEE Int. Conf. on Fuzzy Systems, FUZZ-IEEE 1999 (1999)

    Google Scholar 

  8. Kaufman, L., Rousseeuw, P.: Finding groups in data, an introduction to cluster analysis. John Wiley & Sons, Brussels (1990)

    Book  Google Scholar 

  9. Ng, R., Han, J.: Efficient and effective clustering methods for spatial data mining. In: Proc. of the 20th Very Large DataBases Conference, VLDB 1994, pp. 144–155 (1994)

    Google Scholar 

  10. Hathaway, R., Bezdek, J.: Extending fuzzy and probabilistic clustering to very large data sets. Computational Statistics & Data Analysis 51, 215–234 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hore, P., Hall, L., Goldgof, D.: A cluster ensemble framework for large data sets. Pattern Recognition 42, 676–688 (2009)

    Article  MATH  Google Scholar 

  12. Farnstrom, F., Lewis, J., Elkan, C.: Scalability for clustering algorithms revisited. SIGKDD Explorations 2(1), 51–57 (2000)

    Article  Google Scholar 

  13. Hore, P., Hall, L., Goldgof, D.: Single pass fuzzy c means. In: Proc. of the IEEE Int. Conf. on Fuzzy Systems, FUZZ-IEEE 2007, pp. 1–7 (2007)

    Google Scholar 

  14. Hore, P., Hall, L., Goldgof, D., Cheng, W.: Online fuzzy c means. In: Proc. of NAFIPS 2008, 1–5 (2008)

    Google Scholar 

  15. Ester, M., Kriegel, H.P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proc. of the 24th Very Large DataBases Conference, VLDB 1998, pp. 323–333 (1998)

    Google Scholar 

  16. Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: Proc. of the ACM Int. Conf on Management of Data, SIGMOD 1996, pp. 103–114. ACM Press (1996)

    Google Scholar 

  17. Guha, S., Rastogi, R., Shim, K.: CURE: an efficient clustering algorithm for large databases. In: Proc. of the ACM Int. Conf on Management of Data, SIGMOD 1998, pp. 73–84 (1998)

    Google Scholar 

  18. Bradley, P., Fayyad, U., Reina, C.: Scaling clustering algorithms to large databases. In: Proc. of KDD 1998, pp. 9–15. AAAI Press (1998)

    Google Scholar 

  19. Sander, J., Ester, M., Kriegel, H.P., Xu, X.: Density-based clustering in spatial databases: the algorithm DBSCAN and its application. Data Mining and Knowledge Discovery 2(2), 169–194 (1998)

    Article  Google Scholar 

  20. Hathaway, R., Bezdek, J.: Nerf c-means: non euclidean relational fuzzy clustering. Pattern Recognition 27, 429–437 (1994)

    Article  Google Scholar 

  21. Hathaway, R., Bezdek, J., Davenport, J.: On relational data versions of c-means algorithms. Pattern Recognition Letters 17, 607–612 (1996)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. LIP6, Université Pierre et Marie Curie-Paris 6, UMR7606, 4 place Jussieu, Paris cedex 05, 75252, France

    Marie-Jeanne Lesot & Adrien Revault d’Allonnes

Authors
  1. Marie-Jeanne Lesot
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  2. Adrien Revault d’Allonnes
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, Marburg University, Hans-Meerwein-Strasse 6, 35032, Marburg, Germany

    Eyke Hüllermeier , Thomas Fober  & Bernhard Seeger ,  & 

  2. Department of Computer Science, Auckland University, 38 Princes St., 1010, Auckland, New Zealand

    Sebastian Link

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© 2012 Springer-Verlag Berlin Heidelberg

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Lesot, MJ., Revault d’Allonnes, A. (2012). Credit-Card Fraud Profiling Using a Hybrid Incremental Clustering Methodology. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_25

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  • DOI: https://doi.org/10.1007/978-3-642-33362-0_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33361-3

  • Online ISBN: 978-3-642-33362-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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