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On the Discovery of Exception Rules: A Survey

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Quality Measures in Data Mining

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References

  1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In Peter Buneman and Sushil Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pages 207-216, Washington, D.C., 26-28 1993.

    Google Scholar 

  2. J. Azé. Une nouvelle mesure de qualité pour l’extraction de pépites de connaissances. Extraction des connaissances et apprentissage, 17(1):171-182, 2003.

    Google Scholar 

  3. J.-F. Boulicaut, A. Bykowski, and B. Jeudy. Towards the tractable discovery of association rules with negations. In Proceedings of the Fourth International Conference on Flexible Query Answering Systems, FQAS 2000, pages 425-434, 2000.

    Google Scholar 

  4. S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalizing association rules to correlations. In Proceedings of the ACM SIGMOD International Conference on Management of Data, volume 26,2 of SIGMOD Record, pages 265-276, New York, May13-15 1997. ACM Press.

    Google Scholar 

  5. R. Gras and A. Lahrer. L’implication statistique: une nouvelle méthode d’analyse des données. Mathématiques, Informatique et Sciences Humaines, 120:5-31, 1993.

    Google Scholar 

  6. S. Guillaume, F. Guillet, and J. Philippe. Improving the discovery of association rules with intensity of implication. In PKDD ’98: Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery, pages 318-327, London, UK, 1998. Springer-Verlag.

    Google Scholar 

  7. Z. He, X. Xu, and S. Deng. Data mining for actionable knowledge: A survey. Technical report, Harbin Institute of Technology China, 2005.

    Google Scholar 

  8. F. Hussain, H. Liu, E. Suzuki, and H. Lu. Exception rule mining with a relative interestingness measure. In PAKDD: Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, pages 86-97. LNCS, 2000.

    Google Scholar 

  9. H. Liu, H. Lu, L. Feng, and F. Hussain. Efficient search of reliable exceptions. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 194-203, 1999.

    Google Scholar 

  10. P. M. Murphy and D. W. Aha. UCI Repository of Machine Learning Databases. Machine-readable collection, Dept of Information and Computer Science, University of California, Irvine, 1995. [Available by anonymous ftp from ics.uci.edu in directory pub/machine-learning-databases].

    Google Scholar 

  11. B. Padmanabhan and A. Tuzhilin. A belief-driven method for discovering unexpected patterns. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 94-100, 1998.

    Google Scholar 

  12. B. Padmanabhan and A. Tuzhilin. Unexpectedness as a measure of interestingness in knowledge discovery. Decis. Support Syst., 27(3):303-318, 1999.

    Article  Google Scholar 

  13. B. Padmanabhan and A. Tuzhilin. Small is beautiful: discovering the minimal set of unexpected patterns. In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining, pages 54-63, 2000.

    Google Scholar 

  14. G. Piatetsky-Shapiro and C. J. Matheus. The interestingness of deviations. In Proceedings of the Knowledge Discovery in Databases Workshop (KDD-94), pages 25 - 36, 1994.

    Google Scholar 

  15. A. Savasere, E. Omiecinski, and S. B. Navathe. Mining for strong negative associations in a large database of customer transactions. In International Conference on Data Engineering (ICDE 1998), pages 494-502, 1998.

    Google Scholar 

  16. A. Silberschatz and A. Tuzhilin. On subjective measures of interestingness in knowledge discovery. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pages 275-281, 1995.

    Google Scholar 

  17. A. Silberschatz and A. Tuzhilin. What makes patterns interesting in knowledge discovery systems. IEEE Trans. On Knowledge And Data Engineering, 8:970-974,1996.

    Article  Google Scholar 

  18. P. Smyth and R. M. Goodman. An information theoretic approach to rule induction from databases. IEEE Trans. Knowledge And Data Engineering, 4:301-316,1992.

    Article  Google Scholar 

  19. E. Suzuki. Discovering unexpected exceptions: A stochastic approach. In Proceedings of the fourth international workshop on RSFD, pages 225-232, 1996.

    Google Scholar 

  20. E. Suzuki. Autonomous discovery of reliable exception rules. In David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy Uthurusamy, editors, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), page 259. AAAI Press, 1997.

    Google Scholar 

  21. E. Suzuki. Scheduled discovery of exception rules. In Setsuo Arikawa and Koichi Furukawa, editors, Proceedings of the 2nd International Conference on Discovery Science (DS-99), volume 1721 of LNAI, pages 184-195, Berlin, December 6-8 1999. Springer.

    Google Scholar 

  22. E. Suzuki. Discovering interesting exception rules with rule pair. In PKDD Workshop on Advances in Inductive Rule Learning, pages 163-177, 2004.

    Google Scholar 

  23. E. Suzuki. Evaluation Scheme for Exception Rule/Group Discovery. In Ning Zhong and Jiming Liu, editors, Intelligent Technologies for Information Analysis, pages 89-108, Berlin, 2004. Springer-Verlag.

    Google Scholar 

  24. E. Suzuki and Y. Kodratoff. Discovery of surprising exception rules based on intensity of implication. In Jan M. Zytkow and Mohamed Quafafou, editors, Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD-98), volume 1510 of LNAI, pages 10-18, Berlin, September 23-26 1998. Springer.

    Google Scholar 

  25. E. Suzuki and M. Shimura. Exceptional knowledge discovery in databases based on information theory. In Evangelos Simoudis, Jia Wei Han, and Usama Fayyad, editors, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 275-278. AAAI Press, 1996.

    Google Scholar 

  26. X. Wu, C. Zhang, and S. Zhang. Efficient mining of both positive and negative association rules. ACM Trans. Inf. Syst., 22(3):381-405, 2004.

    Article  Google Scholar 

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Duval, B., Salleb, A., Vrain, C. (2007). On the Discovery of Exception Rules: A Survey. In: Guillet, F.J., Hamilton, H.J. (eds) Quality Measures in Data Mining. Studies in Computational Intelligence, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44918-8_4

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