- 1.Adomavicius, G. and Tuzhilin, A. "User profiling in personalization applications through rule discovery and validation." KDD-99, 1999.]] Google ScholarDigital Library
- 2.Agrawal, R. and Srikant, R. "Fast algorithms for mining association rules." VLDB-94.]] Google ScholarDigital Library
- 3.Bayardo, R., Agrawal, R, and Gunopulos, D. "Constraintbased rule mining in large, dense databases." ICDE-99.]]Google Scholar
- 4.Compton, P. and Jansen, R. Knowledge in context: a strategy for expert system maintenance. AI-88, 1988.]] Google ScholarDigital Library
- 5.Dong, G. and Li, J. "Interestingness of discovered association rules in terms of neighborhood-based unexpectedness," PAKDD-98. 1998.]] Google ScholarDigital Library
- 6.Everitt, B. S. The analysis of contingency tables. Chapman and Hall, 1977.]]Google ScholarCross Ref
- 7.Fayyad, U. M. and Irani, K. B. "Multi-interval discretization of continuous-valued attributes for classification learning." IJCAI-93, 1993.]]Google Scholar
- 8.Han, J. and Fu, Y. "Discovery of multiple-level association rules from large databases." VLDB-95, 1995.]] Google ScholarDigital Library
- 9.Klemetinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkamo, A." "Finding interesting rules from large sets of discovered association rules." CIKM-1994.]] Google ScholarDigital Library
- 10.Kohavi, R., John, G., Long, R., Manley, D., and Pfleger, K. "MLC++: a machine learning library in C++." Tools with artificial intelligence, 1994.]]Google Scholar
- 11.Large, A. Tedd, L. & Hartley, R Information seeking in the online age: principles and practice. Bowker. 1999.]]Google Scholar
- 12.Liu, B., Hsu, W. "Post-analysis of learnt rules"- AAAI-96.]]Google Scholar
- 13.Liu, B., Hsu, W. and Ma, Y. "Integrating classification and association rule mining." KDD-98, 1998.]]Google Scholar
- 14.Liu, B., Hsu, W and Ma, Y. "Pruning and Summarizing the discovered associations." KDD-99, 1999.]] Google ScholarDigital Library
- 15.Liu, B., Hu. M., and Hsu, W. "Intuitive representation of decision trees as general rules and exceptions." AAAI-2000.]] Google ScholarDigital Library
- 16.Liu, H., Lu, H., Feng, F and Hussain, F. "Efficient search of reliable exceptions." PAKDD-99, 1999.]] Google ScholarDigital Library
- 17.Merz, C. J. & Murphy, P. UCI repository of ML databases, 1996. {http://www.cs.uc"edu/~mlearn/MLRepository.html}.]]Google Scholar
- 18.Ng. R. T. Lakshmanan, L., and Han, J. "Exploratory mining and pruning optimizations of constrained association rules." SIGMOD-98, 1998.]] Google ScholarDigital Library
- 19.Padmanabhan, B., and Tuzhilin, A. "A belief-driven method for discovering unexpected patterns." KDD-98.]]Google Scholar
- 20.Pazzani, M., Mani, S. and Shankle, W. R. "Beyond concise and colorful: learning intelligible rules." KDD-97, 1997.]]Google Scholar
- 21.Piatesky-Shapiro, G., and Matheus, C. "The interestingness of deviations." KDD-94.]]Google Scholar
- 22.Quinlan, R. C4.5: program for machine learning. Morgan Kaufmann, 1992.]] Google ScholarDigital Library
- 23.Silberschatz, A., and Tuzhilin, A. "What makes patterns interesting in knowledge discovery systems." IEEE Trans. on Know. and Data Eng. 8(6), 1996.]] Google ScholarDigital Library
- 24.Srikant, R., Vu, Q. and Agrawal, R. "Mining association rules with item constraints." KDD-97.]]Google Scholar
- 25.Suzuki, E. "Autonomous discovery of reliable exception rules." KDD-97, 1997.]]Google Scholar
Index Terms
- Multi-level organization and summarization of the discovered rules
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