Multi-level organization and summarization of the discovered rules

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Multi-level organization and summarization of the discovered rules

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Boston, Massachusetts, United States

Pages: 208 - 217

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Bing Liu	School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543
Minqing Hu	School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543
Wynne Hsu	School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
AAAI : Am Assoc for Artifical Intelligence
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data

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ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 6, Downloads (12 Months): 52, Citation Count: 16

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references cited by index terms collaborative colleagues

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Gediminas Adomavicius , Alexander Tuzhilin, User profiling in personalization applications through rule discovery and validation, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.377-381, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312287]
	2	Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
	3	Bayardo, R., Agrawal, R, and Gunopulos, D. "Constraintbased rule mining in large, dense databases." ICDE-99.
	4	P. Compton , R. Jansen, Knowledge in context: a strategy for expert system maintenance, Proceedings of the second Australian joint conference on Artificial intelligence, p.292-306, March 1990, Adelaide, Australia
	5	Guozhu Dong , Jinyan Li, Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness, Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining, p.72-86, April 15-17, 1998
	6	Everitt, B. S. The analysis of contingency tables. Chapman and Hall, 1977.
	7	Fayyad, U. M. and Irani, K. B. "Multi-interval discretization of continuous-valued attributes for classification learning." IJCAI-93, 1993.
	8	Jiawei Han , Yongjian Fu, Discovery of Multiple-Level Association Rules from Large Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.420-431, September 11-15, 1995
	9	Mika Klemettinen , Heikki Mannila , Pirjo Ronkainen , Hannu Toivonen , A. Inkeri Verkamo, Finding interesting rules from large sets of discovered association rules, Proceedings of the third international conference on Information and knowledge management, p.401-407, November 29-December 02, 1994, Gaithersburg, Maryland, United States [doi>10.1145/191246.191314]
	10	Kohavi, R., John, G., Long, R., Manley, D., and Pfleger, K. "MLC++: a machine learning library in C++." Tools with artificial intelligence, 1994.
	11	Large, A. Tedd, L. & Hartley, R Information seeking in the online age: principles and practice. Bowker. 1999.
	12	Liu, B., Hsu, W. "Post-analysis of learnt rules"- AAAI-96.
	13	Liu, B., Hsu, W. and Ma, Y. "Integrating classification and association rule mining." KDD-98, 1998.
	14	Bing Liu , Wynne Hsu , Yiming Ma, Pruning and summarizing the discovered associations, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.125-134, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312216]
	15	Bing Liu , Minqing Hu , Wynne Hsu, Intuitive Representation of Decision Trees Using General Rules and Exceptions, Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, p.615-620, July 30-August 03, 2000
	16	Huan Liu , Hongjun Lu , Ling Feng , Farhad Hussain, Efficient Search of Reliable Exceptions, Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, p.194-203, April 26-28, 1999
	17	Merz, C. J. & Murphy, P. UCI repository of ML databases, 1996. {http://www.cs.uc"edu/~mlearn/MLRepository.html}.
	18	Raymond T. Ng , Laks V. S. Lakshmanan , Jiawei Han , Alex Pang, Exploratory mining and pruning optimizations of constrained associations rules, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.13-24, June 01-04, 1998, Seattle, Washington, United States
	19	Padmanabhan, B., and Tuzhilin, A. "A belief-driven method for discovering unexpected patterns." KDD-98.
	20	Pazzani, M., Mani, S. and Shankle, W. R. "Beyond concise and colorful: learning intelligible rules." KDD-97, 1997.
	21	Piatesky-Shapiro, G., and Matheus, C. "The interestingness of deviations." KDD-94.
	22	J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
	23	Avi Silberschatz , Alexander Tuzhilin, What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, v.8 n.6, p.970-974, December 1996 [doi>10.1109/69.553165]
	24	Srikant, R., Vu, Q. and Agrawal, R. "Mining association rules with item constraints." KDD-97.
	25	Suzuki, E. "Autonomous discovery of reliable exception rules." KDD-97, 1997.

CITED BY 16

	Wynne Hsu , Mong Li Lee , Bing Liu , Tok Wang Ling, Exploration mining in diabetic patients databases: findings and conclusions, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.430-436, August 20-23, 2000, Boston, Massachusetts, United States
	Chidanand Apte , Bing Liu , Edwin P. D. Pednault , Padhraic Smyth, Business applications of data mining, Communications of the ACM, v.45 n.8, August 2002
	Fernando Berzal , Juan-Carlos Cubero , Daniel Sánchez , José María Serrano, ART: A Hybrid Classification Model, Machine Learning, v.54 n.1, p.67-92, January 2004
	Deborah R. Carvalho , Alex A. Freitas, A hybrid decision tree/genetic algorithm method for data mining, Information Sciences: an International Journal, v.163 n.1-3, p.13-35, 14 June 2004
	Bing Liu , Wynne Hsu , Shu Chen , Yiming Ma, Analyzing the Subjective Interestingness of Association Rules, IEEE Intelligent Systems, v.15 n.5, p.47-55, September 2000
	Bing Liu , Wynne Hsu , Shu Chen , Yiming Ma, Analyzing the Subjective Interestingness of Association Rules, IEEE Intelligent Systems, v.15 n.5, p.47-55, September 2000
	Kok-Leong Ong , Wee-Keong Ng , Ee-Peng Lim, CrystalBall: a framework for mining variants of association rules, Proceedings of the fourteenth Australasian database conference, p.85-94, February 01, 2003, Adelaide, Australia
	Yu Chen , Lars Henning Pedersen , Wesley W. Chu , Jorn Olsen, Drug exposure side effects from mining pregnancy data, ACM SIGKDD Explorations Newsletter, v.9 n.1, June 2007
	Jiong Yang , Wei Wang , Philip S. Yu, Mining Surprising Periodic Patterns, Data Mining and Knowledge Discovery, v.9 n.2, p.189-216, September 2004
	Yiming Ma , Bing Liu , Ching Kian Wong, Web for data mining: organizing and interpreting the discovered rules using the Web, ACM SIGKDD Explorations Newsletter, v.2 n.1, p.16-23, June, 2000
	Andrew Secker , Alex A. Freitas , Jon Timmis, AISIID: An artificial immune system for interesting information discovery on the web, Applied Soft Computing, v.8 n.2, p.885-905, March, 2008
	Du Zhang , Quoc Luan Ha , Meiliu Lu, Mining California vital statistical data, International Journal of Computer Applications in Technology, v.27 n.4, p.281-297, October 2006
	Mafruz Zaman Ashrafi , David Taniar , Kate Smith, Redundant association rules reduction techniques, International Journal of Business Intelligence and Data Mining, v.2 n.1, p.29-63, March 2007
	Gavin Shaw , Yue Xu , Shlomo Geva, Deriving non-redundant approximate association rules from hierarchical datasets, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA
	Yen-Liang Chen , Lucas Tzu-Hsuan Hung, Using decision trees to summarize associative classification rules, Expert Systems with Applications: An International Journal, v.36 n.2, p.2338-2351, March, 2009
	Varun Chandola , Vipin Kumar, Summarization – compressing data into an informative representation, Knowledge and Information Systems, v.12 n.3, p.355-378, August 2007