Undirected discovery of interesting exception rules

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 16, Issue 8, 2002, Pages 1065-1086

  Suzuki, Einoshin ^a  

^a YOKOHAMA NATIONAL UNIVERSITY   (Japan)

Author keywords

Exception rule; Knowledge discovery; Objective evaluation of interestingness; Probabilistic estimation; Rule discovery

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; ARTIFICIAL INTELLIGENCE; DATABASE SYSTEMS; ESTIMATION; INFORMATION TECHNOLOGY; LEARNING SYSTEMS; POLYNOMIALS; PROBABILITY DISTRIBUTIONS;

EXCEPTION RULE; KNOWLEDGE DISCOVERY; MULTINOMIAL DISTRIBUTIONS; PROBABILISTIC ESTIMATION; RULE DISCOVERY;

KNOWLEDGE BASED SYSTEMS;

EID: 0036978502     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001402002155     Document Type: Article

References (33)

1. G. Adomavicius and A. Tuzhilin, "Discovery of actionable patterns in databases: the action hierarchy approach," Proc. Third Int. Conf. Knowledge Discovery and Data Mining (KDD), 1997, 1997, pp. 111-114.
2. R. Agrawal et al., "Fast discovery of association rules," Advances in Knowledge Discovery and Data Mining, eds. U. M. Fayyad et al., AAAI/MIT Press, Menlo Park, CA, 1996, pp. 307-328.
3. C. Blake, C. J. Merz and E. Keogh, "UCI repository of machine learning databases," http://www.ics.uci.edu/~mlearn/MLRepository.html, University of California, Irvine, Dept. Information and CS (current May 5, 1999).
4. S. Brin et al., "Dynamic itemset counting and implication rules for market basket data," Proc. ACM SIGMOD Int. Conf. Management of Data, 1997, pp. 255-264.
5. H. Cramér, Mathematical Methods of Statistics, Princeton University Press, NJ, 1966, p. 120.
6. J. Dougherty, R. Kohavi and M. Sahami, "Supervised and unsupervised discretization of continuous features," Proc. Twelfth Int. Conf. Machine Learning (ICML), Morgan Kaufmann, San Francisco, 1995, pp. 194-202.
7. U. M. Fayyad and K. B. Irani, "Multi-interval discretization of continuous-valued attributes for classification learning," Proc. Thirteenth Int. Joint Conf. Artificial Intelligence (IJCAI), Morgan Kaufmann, San Mateo, CA, 1993, pp. 1022-1027.
8. U. M. Fayyad, G. Piatetsky-Shapiro and P. Smyth, "From data mining to knowledge discovery: an overview," Advances in Knowledge Discovery and Data Mining, eds. U. M. Fayyad et al., AAAI/MIT Press, Menlo Park, CA 1996, pp. 1-34.
9. W. J. Frawley, G. Piatetsky-Shapiro and C. J. Matheus, "Knowledge discovery in databases: an overview," Knowledge Discovery in Databases, eds. G. Piatetsky-Shapiro and W. J. Frawley, AAAI/MIT Press Menlo Park, CA, 1991, pp. 1-27.
10. J. Fürnkranz, "Separate-and-conquer rule learning," Artif. Intell. Rev. 13 (1999) 3-54
11. R. Gras, L' Implication Statistique, La Pensée Sauvage, France, 1996 (in French).
12. F. Hussain et al., "Exception rule mining with a relative interestingness measure," Knowledge Discovery and Data Mining, LNAI, Vol. 1805 (PAKDD), Springer, Berlin, 2000, pp. 86-97.
13. D. Jensen and P. Cohen, "Multiple comparisons in induction algorithms," Mach. Learn. 38 (2000) 309-338.
14. M. Klemettinen et al., "Finding interesting rules from large sets of discovered association rules," Proc. Third Int. Conf. Information and Knowledge Management (CIKM), 1994, pp. 401-407.
15. W. Klösgen, "Explora: a multipattern and multistrategy discovery approach," Advances in Knowledge Discovery and Data Mining, eds. U. M. Fayyad et al., AAAI/MIT Press, Menlo Park, CA, 1996, pp. 249-271.
16. E. M. Knorr and R. T. Ng, "Algorithms for mining distance-based outliers in large datasets," Proc. 24th Ann. Int. Conf. Very Large Data Bases (VLDB), 1998, pp. 392-403.
17. B. Liu et al., "Finding interesting patterns using user expectations," IEEE Trans. Knowl. Data Eng. 11, pp. 817-832.
18. B. Liu, W. Hsu and Y. Ma, "Pruning and summarizing the discovered associations," Proc. Fifth ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), 1999, pp. 125-134.
19. J. A. Major and J. J. Magno, "Selecting among rules induced from a hurricane database," Proc. AAAI-93 Workshop Knowledge Discovery in Databases, 1993, pp. 28-44.
20. B. Padmanabhan and A. Tuzhilin, "A belief-driven method for discovering unexpected patterns," Proc. Fourth Int. Conf. Knowledge Discovery and Data Mining (KDD), AAAI Press, Menlo Park, CA, 1998, pp. 94-100.
21. G. Piatetsky-Shapiro, "Discovery, analysis, and presentation of strong rules," Knowledge Discovery in Databases, eds. G. Piatetsky-Shapiro and W. J. Frawley, AAAI/MIT Press, Menlo Park, CA 1991, pp. 229-248.
22. S. Sarawagi, "Explaining differences in multidimensional aggregates," Proc. 25th Int. Conf. Very Large Data Bases (VLDB), 1999, pp. 42-53.
23. G. Piatetsky-Shapiro and C. J. Matheus, "The interestingness of deviations," AAAI-94 Workshop on Knowledge Discovery in Databases, Tech Rep. WS-94-03, AAAI Press, Menlo Park, CA, 1994, pp. 25-36.
24. A. Silberschatz and A. Tuzhilin, "What makes patterns interesting in knowledge discovery systems," IEEE Trans. Knowl. Data Eng. 8 (1996) 970-974.
25. P. Smyth and R. M. Goodman, "An information theoretic approach to rule induction from databases," IEEE Trans. Knowl. Data Eng. 4 (1992) 301-316.
26. E. Suzuki and M. Shimura, "Exceptional knowledge discovery in databases based on information theory," Proc. Second Int. Conf. Knowledge Discovery and Data Mining (KDD), AAAI Press, Menlo Park, CA, 1996, pp. 275-278.
27. E. Suzuki, "Autonomous discovery of reliable exception rules," Proc. Third Int. Conf. Knowledge Discovery and Data Mining (KDD), AAAI Press, Menlo Park, CA, 1997, pp. 259-262.
28. E. Suzuki, "Scheduled discovery of exception rules," Discovery Science, LNAI, Vol. 1721 (DS), Springer, Berlin, 1999, pp. 184-195.
29. E. Suzuki and S. Tsumoto, "Evaluating hypothesis-driven exception-rule discovery with medical data sets," Knowledge Discovery and Data Mining, LNAI, Vol. 1805 (PAKDD), Springer, Berlin, 2000, pp. 208-211.
30. E. Suzuki, "Mining bacterial test data with scheduled discovery of exception rules," Proc. Int. Workshop of KDD Challenge on Real-world Data (KDD Challenge), Kyoto, Japan, 2000, pp. 34-40.
31. E. Suzuki and J. M. Zytkow, "Unified algorithm for undirected discovery of exception rules," Principles of Data Mining and Knowledge Discovery, LNAI, Vol. 1910 (PKDD), Springer, Berlin, 2000, pp. 169-180.
32. S. Tsumoto et al., "Comparison of data mining methods using common medical datasets," ISM Symp.: Data Mining and Knowledge Discovery in Data Science, 1999, pp. 63-72.
33. N. Yugami, Y. Ohta and S. Okamoto, "Fast discovery of interesting rules," Knowledge Discovery and Data Mining, LNAI, Vol. 1805 (PAKDD), Springer, Berlin, 2000, pp. 17-28.