A Unified Approach for Discovery of Interesting Association Rules in Medical Databases

1. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kauffmann Publishers, San Francisco (2001)

   Google Scholar 

2. Dunham, M.H.: Data Mining: Introductory and Advanced Topics, 1st edn. Pearson ygEducation (Singapore) Pte. Ltd (2003)

   Google Scholar 

3. Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. Prentice-Hall of India Private Limited, India (2001)

   Google Scholar 

4. Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Adaptive Constraint Pushing in Frequent Pattern Mining. In: Proceedings of the 17th European Conference on PAKDD 2003 (2003)

   Google Scholar 

5. Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: ExAMiner: Optimized Level-wise Frequent pattern Mining with Monotone Constraints. In: Proceedings of the 3rd International Conference on Data Mining (ICDM 2003) (2003)

   Google Scholar 

6. Bronchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D.: Exante: Anticipated Data Reduction in Constrained Pattern Mining. In: Proceedings of the 7th PAKDD 2003 (2003)

   Google Scholar 

7. Freitas, A.A.: On Rule Interestingness Measures. Knowledge-Based Systems 12, 309–315 (1999)

   Article  Google Scholar 

8. Klemetinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding Interesting Rules from Large Sets of Discovered Association Rules. In: Proceedings of the 3rd International Conference on Information and Knowledge Management, Gaithersburg, Maryland (1994)

   Google Scholar 

9. Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the Subjective Interestingness of Association Rules. IEEE Intelligent Systems (2000)

   Google Scholar 

10. Liu, B., Hsu, W.: Post Analysis of Learned Rules. In: Proceedings of the 13th National Conference on AI (AAAI 1996) (1996)

    Google Scholar 

11. Liu, B., Hsu, W., Lee, H.-Y., Mum, L.-F.: Tuple-Level Analysis for Identification of Interesting Rules. Technical Report TRA5/95, SoC. National University of Singapore, Singapore (1996)

    Google Scholar 

12. Liu, B., Hsu, W.: Finding Interesting Patterns Using User Expectations. DISCS Technical Report (1995)

    Google Scholar 

13. Liu, B., Hsu, W., Chen, S.: Using General Impressions to Analyze Discovered Classification Rules. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data mining (KDD 1997) (1997)

    Google Scholar 

14. Padmanabhan, B., Tuzhilin, A.: Unexpectedness as a Measure of Interestingness in Knowledge Discovery. Working paper # IS-1997. Dept. of Information Systems, Stern School of Business, NYU (1997)

    Google Scholar 

15. Padmanabhan, B., Tuzhilin, A.: A Belief-Driven Method for Discovering Unexpected Patterns. In: KDD 1998 (1998)

    Google Scholar 

16. Padmanabhan, B., Tuzhilin, A.: Small is Beautiful: Discovering the Minimal Set of Unexpected Patterns. In: KDD 2000 (2000)

    Google Scholar 

17. Piatetsky-Shapiro, G., Matheus, C.J.: The Interestingness of Deviations. In: Proceedings of AAAI Workshop on Knowledge Discovery in Databases (1994)

    Google Scholar 

18. Piatetsky-Shapiro, G.: Discovery, Analysis, and Presentation of Strong Rules. In: Knowledge Discovery in Databases. The AAAI Press, Menlo Park (1991)

    Google Scholar 

19. Psaila, G.: Discovery of Association Rule Meta-patterns. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 219–228. Springer, Heidelberg (1999)

    Google Scholar 

20. Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: ACM SIGMOD Conference of Management of Data, Washington D.C (1993)

    Google Scholar 

21. Silberschatz, A., Tuzhilin, A.: On Subjective Measures of Interestingness in Knowledge Discovery. In: Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (1995)

    Google Scholar 

22. Silberschatz, A., Tuzhilin, A.: What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Trans. and Data Engineering 5(6) (1996)

    Google Scholar 

23. Suzuki, E., Kodratoff, Y.: Discovery of Surprising Exception Rules Based on Intensity of Implication. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS (LNAI), vol. 1510. Springer, Heidelberg (1998)

    Google Scholar 

24. Liu, B., Hsu, W., Chen, S., Ma, Y.: Analyzing the Subjective Interestingness of Association Rules. IEEE Intelligent Systems (2000)

    Google Scholar 

25. Al-Hegami, A.S., Bhatnagar, V., Kumar, N.: Novelty Framework for Knowledge Discovery in Databases. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 48–57. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

26. Bhatnagar, V., Al-Hegami, A.S., Kumar, N.: Novelty as a Measure of Interestingness in Knowledge Discovery. International Journal of Information Technology 2(1) (2005)

    Google Scholar 

27. Kovalerchuk, B., Triantaphyllou, E., Despande, A., Vtyaev, E.: Interactive Learning of Monotone Boolean Function. Information Sciences 94(1-4), 87–118 (1996)

    Article  Google Scholar 

28. Hansel, G.: Sur le nombre des functions Boolenes Monotones den variables. C.R. Acad. Sci. 262(20), 1088–1090 (1966) (in French)

    MathSciNet  Google Scholar 

29. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

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