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International Conference on Flexible Query Answering Systems

FQAS 2013: Flexible Query Answering Systems pp 143–154Cite as

Detecting Anomalous and Exceptional Behaviour on Credit Data by Means of Association Rules

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8132))

Abstract

Association rules is a data mining technique for extracting useful knowledge from databases. Recently some approaches has been developed for mining novel kinds of useful information, such us peculiarities, infrequent rules, exception or anomalous rules. The common feature of these proposals is the low support of such type of rules. Therefore, finding efficient algorithms for extracting them are needed.

The aim of this paper is three fold. First, it reviews a previous formulation for exception and anomalous rules, focusing on its semantics and definition. Second, we propose efficient algorithms for mining such type of rules. Third, we apply them to the case of detecting anomalous and exceptional behaviours on credit data.

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References

  1. Agrawal, R., Manilla, H., Sukent, R., Toivonen, A., Verkamo, A.: Fast discovery of Association rule. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328. AAA Press (1996)

    Google Scholar 

  2. Balderas, M.A., Berzal, F., Cubero, J.C., Eisman, E., Marín, N.: Discovering hidden association rules. In: KDD Workshop on Data Mining Methods for Anomaly Detection, Chicago, pp. 13–20 (2005)

    Google Scholar 

  3. Berzal, F., Cubero, J.C., Marín, N., Gámez, M.: Anomalous association rules. In: IEEE ICDM Workshop Alternative Techniques for Data Mining and Knowledge Discovery (2004)

    Google Scholar 

  4. Berzal, F., Delgado, M., Sánchez, D., Vila, M.A.: Measuring accuracy and interest of association rules: A new framework. Intelligent Data Analysis 66(3), 221–235 (2002)

    Google Scholar 

  5. Delgado, M., Marín, N., Sánchez, D., Vila, M.A.: Fuzzy association rules: General model and applications. IEEE Trans. on Fuzzy Systems 11(2), 214–225 (2003)

    Article  Google Scholar 

  6. Delgado, M., Ruiz, M.D., Sánchez, D.: Studying interest measures for association rules through a logical model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18(1), 87–106 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  7. Delgado, M., Ruiz, M.D., Sánchez, D.: New approaches for discovering exception and anomalous rules. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19(2), 361–399 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ding, J., Yau, S.S.T.: TCOM, an innovative data structure for mining association rules among infrequent items. Computers & Mathematics with Applications 57(2), 290–301 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  9. Duval, B., Salleb, A., Vrain, C.: On the discovery of exception rules: A survey. Studies in Computational Intelligence 43, 77–98 (2007)

    Article  Google Scholar 

  10. Fawcet, T., Provost, F.: Adaptative fraud detection. In: Data Mining and Knowledge Discovery, pp. 291–316 (1997)

    Google Scholar 

  11. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38(3), 9 (2006)

    Article  Google Scholar 

  12. Hussain, F., Liu, H., Suzuki, E., Lu, H.: Exception rule mining with a relative interestingness measure. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 86–97. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  13. Liu, H., Lu, H., Feng, L., Hussain, F.: Efficient search of reliable exceptions. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 194–204. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  14. Louie, E., Lin, T.Y.: Finding association rules using fast bit computation: Machine-oriented modeling. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 486–494. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  15. Padmanabhan, B., Tuzhilin, A.: A belief driven method for discovering unexpected patterns. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 94–100 (1998)

    Google Scholar 

  16. Rauch, J., Šimunek, M.: An alternative approach to mining association rules. Studies in Computational Intelligence (SCI) 6, 211–231 (2005)

    Google Scholar 

  17. Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Mathematical Biosciences 23, 351–379 (1975)

    Article  MathSciNet  Google Scholar 

  18. Silberschatz, A., Tuzhilin, A.: User-assisted knowledge discovery: how much should the user be involved. In: ACM-SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (1996)

    Google Scholar 

  19. Sim, A.T.H., Indrawan, M., Srinivasan, B.: Mining infrequent and interesting rules from transaction records. In: 7th WSEAS Int. Conf. on AI, Knowledge Engineering and Databases (AIKED 2008), pp. 515–520 (2008)

    Google Scholar 

  20. Suzuki, E.: Discovering unexpected exceptions: A stochastic approach. In: Proceedings of the Fourth International Workshop on RSFD, pp. 225–232 (1996)

    Google Scholar 

  21. Suzuki, E.: Undirected discovery of interesting exception rules. International Journal of Pattern Recognition and Artificial Intelligence 16(8), 1065–1086 (2002)

    Article  Google Scholar 

  22. Suzuki, E.: Discovering interesting exception rules with rule pair. In: Proc. Workshop on Advances in Inductive Rule Learning at PKDD 2004, pp. 163–178 (2004)

    Google Scholar 

  23. Suzuki, E., Shimura, M.: Exceptional knowledge discovery in databases based on information theory. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 275–278. AAAI Press (1996)

    Google Scholar 

  24. Taniar, D., Rahayu, W., Lee, V., Daly, O.: Exception rules in association rule mining. Applied Mathematics and Computation 205, 735–750 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  25. Yao, Y., Wang, F.Y., Zeng, D., Wang, J.: Rule + exception strategies for security information analysis. IEEE Intelligent Systems, 52–57 (2005)

    Google Scholar 

  26. Zhong, N., Ohshima, M., Ohsuga, S.: Peculiarity oriented mining and its application for knowledge discovery in amino-acid data. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol. 2035, pp. 260–269. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  27. Zhou, L., Yau, S.: Efficient association rule mining among both frequent and infrequent items. Computers & Mathematics with Applications 54(6), 737–749 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Dpt. Computer Science and A.I., CITIC-UGR, University of Granada, C/Periodista Daniel Saucedo Aranda s/n, Granada, 18071, Spain

    Miguel Delgado, Maria J. Martin-Bautista, M. Dolores Ruiz & Daniel Sánchez

  2. European Centre for Soft Computing, Mieres, Spain

    Daniel Sánchez

Authors
  1. Miguel Delgado
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  2. Maria J. Martin-Bautista
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  3. M. Dolores Ruiz
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  4. Daniel Sánchez
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Editor information

Editors and Affiliations

  1. Department of Electronic Systems, Aalborg University, 6700, Esbjerg, Denmark

    Henrik Legind Larsen

  2. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Maria J. Martin-Bautista

  3. Department of Computer Science and Arificial IntelIigence, University of Granada, 18071, Granada, Spain

    María Amparo Vila

  4. CBIT, Roskilde University, Universitetsvej 1, 4000, Roskilde, Denmark

    Troels Andreasen

  5. CBIT, Roskilde University, 4000, Roskilde, Denmark

    Henning Christiansen

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© 2013 Springer-Verlag Berlin Heidelberg

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Delgado, M., Martin-Bautista, M.J., Ruiz, M.D., Sánchez, D. (2013). Detecting Anomalous and Exceptional Behaviour on Credit Data by Means of Association Rules. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_13

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  • DOI: https://doi.org/10.1007/978-3-642-40769-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40768-0

  • Online ISBN: 978-3-642-40769-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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