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Mining Exceptional Relationships Between Patterns

  • Sebastián Ventura
  • José María Luna
Chapter
  • 988 Downloads

Abstract

In any dataset, it is possible to identify small subsets of data which distribution is exceptionally different from the distribution in the complete set of data records. Finding such exceptional behaviour it is possible to mine interesting associations, which is known as the exceptional relationship mining task. This chapter formally describes the task, which is considered as a special process that lies in the intersection of both exceptional model mining and association rule mining. The current chapter first analyses the exceptional model mining task. Then, it formally describes the task of mining exceptional relationships. Finally, this chapter deals with a model based on grammars and some applications where the extraction of exceptional relationships is justified.

Keywords

Association Rule Genetic Operator Pattern Mining Association Rule Mining Target Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    C. C. Aggarwal and J. Han. Frequent Pattern Mining. Springer International Publishing, 2014.CrossRefzbMATHGoogle Scholar
  2. 2.
    R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD Conference ’93, pages 207–216, Washington, DC, USA, 1993.Google Scholar
  3. 3.
    M. Atzmueller. Subgroup Discovery - Advanced Review. WIREs: Data Mining and Knowledge Discovery, 5:35–49, 2015.Google Scholar
  4. 4.
    W. Duivesteijn, A. J. Knobbe, A. Feelders, and M. van Leeuwen. Subgroup discovery meets bayesian networks – an exceptional model mining approach. In Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM 2010, pages 158–167, Sydney, Australia, December 2010. IEEE Computer Society.Google Scholar
  5. 5.
    W. Duivesteijn, A. J. Feelders, and A. Knobbe. Exceptional model mining. Data Mining and Knowledge Discovery, 30(1):47–98, 2015.MathSciNetCrossRefGoogle Scholar
  6. 6.
    D. Dumitrescu, B. Lazzerini, L. C. Jain, and A. Dumitrescu. Evolutionary Computation. CRC Press, Inc., Boca Raton, FL, USA, 2000.zbMATHGoogle Scholar
  7. 7.
    D. Freedman, R. Pisani, and R. Purves. Statistics (4th edition). W. W. Norton, 2007.Google Scholar
  8. 8.
    B. Goethals, S. Moens, and J. Vreeken. MIME: A Framework for Interactive Visual Pattern Mining. In D. Gunopulos, T. Hofmann, D. Malerba, and M. Vazirgiannis, editors, Machine Learning and Knowledge Discovery in Databases, volume 6913 of Lecture Notes in Computer Science, pages 634–637. Springer Berlin Heidelberg, 2011.Google Scholar
  9. 9.
    M. Gupta, J. Gao, Y. Sun, and J. Han. Community trend outlier detection using soft temporal pattern mining. In P. A. Flach, T. De Bie, and N. Cristianini, editors, Machine Learning and Knowledge Discovery in Databases, volume 7524 of Lecture Notes in Computer Science, pages 692–708. Springer Berlin Heidelberg, 2012.CrossRefGoogle Scholar
  10. 10.
    F. Herrera, C. J. Carmona, P. González, and M. J. del Jesus. An overview on subgroup discovery: Foundations and applications. Knowledge and Information Systems, 29(3): 495–525, 2011.CrossRefGoogle Scholar
  11. 11.
    M. Leeuwen and A. Knobbe. Diverse subgroup set discovery. Data Mining Knowledge Discovery, 25(2):208–242, 2012.MathSciNetCrossRefGoogle Scholar
  12. 12.
    D. Leman, A. Feelders, and A. J. Knobbe. Exceptional model mining. In Proceedings of the European Conference in Machine Learning and Knowledge Discovery in Databases, volume 5212 of ECML/PKDD 2008, pages 1–16, Antwerp, Belgium, 2008. Springer.Google Scholar
  13. 13.
    J. M. Luna, A. Ramirez, J. R. Romero, and S. Ventura. An intruder detection approach based on infrequent rating pattern mining. In Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, ISDA 2010, ISDA 2010, pages 682–688, 2010.Google Scholar
  14. 14.
    J. M. Luna, J. R. Romero, and S. Ventura. Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules. Knowledge and Information Systems, 32(1):53–76, 2012.CrossRefGoogle Scholar
  15. 15.
    J. M. Luna, J. R. Romero, C. Romero, and S. Ventura. Reducing gaps in quantitative association rules: a genetic programming free-parameter algorithm. Integrated Computer Aided Engineering, 21(4):321–337, 2014.Google Scholar
  16. 16.
    J. M. Luna, M. Pechenizkiy, and S. Ventura. Mining exceptional relationships with grammar-guided genetic programming. Knowledge and Information Systems, pages 1–24, In press,2015.Google Scholar
  17. 17.
    Y. Z. Ma. Simpson’s paradox in GDP and per capita GDP growths. Empirical Economics, 49(4):1301–1315, 2015.CrossRefGoogle Scholar
  18. 18.
    D. Martín, A. Rosete, J. Alcalá, and F. Herrera. A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. IEEE Transactions on Evolutionary Computation, 18(1):54–69, 2014.CrossRefGoogle Scholar
  19. 19.
    R. McKay, N. Hoai, P. Whigham, Y. Shan, and M. O’Neill. Grammar-based Genetic Programming: a Survey. Genetic Programming and Evolvable Machines, 11:365–396, 2010.CrossRefGoogle Scholar
  20. 20.
    R. Srikant and R. Agrawal. Mining Quantitative Association Rules in Large Relational Tables. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, SIGMOD’96, Montreal, Quebec, Canada, 1996.Google Scholar
  21. 21.
    M. van Leeuwen. Maximal exceptions with minimal descriptions. Data Mining and Knowledge Discovery, 21(2):259–276, 2010.MathSciNetCrossRefGoogle Scholar
  22. 22.
    C. Zhang and S. Zhang. Association rule mining: models and algorithms. Springer Berlin / Heidelberg, 2002.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastián Ventura
    • 1
  • José María Luna
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain

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