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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
Duval, B., Salleb, A., Vrain, C.: On the discovery of exception rules: A survey. Studies in Computational Intelligence 43, 77–98 (2007)
Fawcet, T., Provost, F.: Adaptative fraud detection. In: Data Mining and Knowledge Discovery, pp. 291–316 (1997)
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38(3), 9 (2006)
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)
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)
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)
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)
Rauch, J., Šimunek, M.: An alternative approach to mining association rules. Studies in Computational Intelligence (SCI) 6, 211–231 (2005)
Shortliffe, E., Buchanan, B.: A model of inexact reasoning in medicine. Mathematical Biosciences 23, 351–379 (1975)
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)
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)
Suzuki, E.: Discovering unexpected exceptions: A stochastic approach. In: Proceedings of the Fourth International Workshop on RSFD, pp. 225–232 (1996)
Suzuki, E.: Undirected discovery of interesting exception rules. International Journal of Pattern Recognition and Artificial Intelligence 16(8), 1065–1086 (2002)
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)
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)
Taniar, D., Rahayu, W., Lee, V., Daly, O.: Exception rules in association rule mining. Applied Mathematics and Computation 205, 735–750 (2008)
Yao, Y., Wang, F.Y., Zeng, D., Wang, J.: Rule + exception strategies for security information analysis. IEEE Intelligent Systems, 52–57 (2005)
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)
Zhou, L., Yau, S.: Efficient association rule mining among both frequent and infrequent items. Computers & Mathematics with Applications 54(6), 737–749 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)