Small is beautiful

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Small is beautiful: discovering the minimal set of unexpected patterns

Full text

Pdf (107 KB)

Source

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: 54 - 63

Year of Publication: 2000

ISBN:1-58113-233-6

Authors

Balaji Padmanabhan	The Wharton School, University of Pennsylvania, 1310 Steinberg-Dietrich Hall, Philadelphia, PA
Alexander Tuzhilin	Stern School of Business, New York University, 44 West 4lt;supgt;thlt;/supgt; Street, New York, NY

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

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 12, Downloads (12 Months): 68, Citation Count: 33

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/347090.347103 What is a DOI?

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	Rakesh Agrawal , Heikki Mannila , Ramakrishnan Srikant , Hannu Toivonen , A. Inkeri Verkamo, Fast discovery of association rules, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
	2	Roberto J. Bayardo, Jr. , Rakesh Agrawal, Mining the most interesting rules, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.145-154, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312219]
	3	Bayardo, R., Agrawal, R. and Gunopulos, D., 1999. Constraint-Based Rule Mining in Large, Dense Databases. In Proceedings of ICDE, 1999.
	4	Berger, G. and Tuzhilin, A., 1998. Discovering Unexpected Patterns in Temporal Data Using Temporal Logic. In Etzion, O., Jajodia, S. and Sripada, S. eds, Temporal Databases: Research and Practice. Springer, 1998.
	5	Soumen Chakrabarti , Sunita Sarawagi , Byron Dom, Mining Surprising Patterns Using Temporal Description Length, Proceedings of the 24rd International Conference on Very Large Data Bases, p.606-617, August 24-27, 1998
	6	Liu, B. and Hsu, W., 1996. Post-Analysis of Learned Rules. In Proc. of the Thirteenth National Conference on Artificial Intelligence (AAAI '96), pp. 828-834.
	7	Liu, B., Hsu, W. and Chen, S, 1997. Using General Impressions to Analyze Discovered Classification Rules. In Proc. of the Third International Conference on Knowledge Discovery and Data Mining (KDD 97), pp. 31-36.
	8	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]
	9	Mitchell, T., 1982. Generalization as Search. Artificial Intelligence, pp. 203-226.
	10	Balaji Padmanabhan , Alexander Tuzhilin, Discovery of unexpected patterns in data mining applications, 1999
	11	Padmanabhan, B. and Tuzhilin, A., 1998. "A Belief-Driven Method for Discovering Unexpected Patterns." In Proc. 4th Int'l Conf. on Know. Discovery and Data Mining, 1998.
	12	Balaji Padmanabhan , Alexander Tuzhilin, Unexpectedness as a measure of interestingness in knowledge discovery, Decision Support Systems, v.27 n.3, p.303-318, Dec.1999 [doi>10.1016/S0167-9236(99)00053-6]
	13	Suzuki, E., 1997. Autonomous Discovery of Reliable Exception Rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 259-262.
	14	Subramonian, R. Defining diff as a data mining primitive. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 1998.
	15	Ramakrishnan Srikant , Rakesh Agrawal, Mining quantitative association rules in large relational tables, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.1-12, June 04-06, 1996, Montreal, Quebec, Canada
	16	Shah, D., Lakshmanan, L.V.S, Ramamritham, K., and Sudarshan, S., 1999. Interestingness and Pruning of Mined Patterns. In Proceedings of the 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), Philadelphia, 1999.
	17	Silberschatz, A. and Tuzhilin, A., 1995. On Subjective Measures of Interestingness in Knowledge Discovery. In Proc. of the First International Conference on Knowledge Discovery and Data Mining, pp. 275-281.
	18	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]
	19	Srikant, R., Vu, Q. and Agrawal, R. Mining Association Rules with Item Constraints. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD 97), pp. 67-73.
	20	Toivonen, H., Klemetinen, M., Ronkainen, P., Hatonen, K. and Mannila, H., 1995. Pruning and Grouping Discovered Association Rules. In MLNet Workshop on Statistics, Machine Learning and Discovery in Databases, pp. 47-52.

CITED BY 33

	Bing Liu , Kaidi Zhao , Lan Yi, Visualizing web site comparisons, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA
	Bing Liu , Yiming Ma , Philip S. Yu, Discovering unexpected information from your competitors' web sites, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.144-153, August 26-29, 2001, San Francisco, California
	Bing Liu , Wynne Hsu , Yiming Ma, Discovering the set of fundamental rule changes, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.335-340, August 26-29, 2001, San Francisco, California
	Bing Liu , Wynne Hsu , Yiming Ma, Identifying non-actionable association rules, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.329-334, August 26-29, 2001, San Francisco, California
	Jiong Yang , Wei Wang , Philip S. Yu, Infominer: mining surprising periodic patterns, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.395-400, August 26-29, 2001, San Francisco, California
	Balaji Padmanabhan , Alexander Tuzhilin, Knowledge refinement based on the discovery of unexpected patterns in data mining, Decision Support Systems, v.33 n.3, p.309-321, July 2002
	Liqiang Geng , Howard J. Hamilton, Interestingness measures for data mining: A survey, ACM Computing Surveys (CSUR), v.38 n.3, p.9-es, 2006
	Hong Zhang , Balaji Padmanabhan , Alexander Tuzhilin, On the discovery of significant statistical quantitative rules, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA
	Xindong Wu , Chengqi Zhang , Shichao Zhang, Efficient mining of both positive and negative association rules, ACM Transactions on Information Systems (TOIS), v.22 n.3, p.381-405, July 2004
	Christopher Jermaine, Finding the most interesting correlations in a database: how hard can it be?, Information Systems, v.30 n.1, p.21-46, March 2005
	Ke Wang , Yuelong Jiang , Laks V. S. Lakshmanan, Mining unexpected rules by pushing user dynamics, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C.
	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
	Szymon Jaroszewicz , Dan A. Simovici, Interestingness of frequent itemsets using Bayesian networks as background knowledge, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA
	Szymon Jaroszewicz , Tobias Scheffer, Fast discovery of unexpected patterns in data, relative to a Bayesian network, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, August 21-24, 2005, Chicago, Illinois, USA
	Li Yang, Pruning and Visualizing Generalized Association Rules in Parallel Coordinates, IEEE Transactions on Knowledge and Data Engineering, v.17 n.1, p.60-70, January 2005
	Anthony Scime , Gregg R. Murray, Vote prediction by iterative domain knowledge and attribute elimination, International Journal of Business Intelligence and Data Mining, v.2 n.2, p.160-176, June 2007
	Aaron Ceglar , John F. Roddick, Association mining, ACM Computing Surveys (CSUR), v.38 n.2, p.5-es, 2006
	Sandra de Amo , Daniel A. Furtado, First-order temporal pattern mining with regular expression constraints, Data & Knowledge Engineering, v.62 n.3, p.401-420, September, 2007
	Zidrina Pabarskaite , Aistis Raudys, A process of knowledge discovery from web log data: Systematization and critical review, Journal of Intelligent Information Systems, v.28 n.1, p.79-104, February 2007
	Monica H. Ou , Geoff A. W. West , Mihai Lazarescu , Chris Clay, Dynamic knowledge validation and verification for CBR teledermatology system, Arificial Intelligence in Medicine, v.39 n.1, p.79-96, January, 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
	Raz Tamir , Yehuda Singer, On a confidence gain measure for association rule discovery and scoring, The VLDB Journal — The International Journal on Very Large Data Bases, v.15 n.1, p.40-52, January 2006
	Cristóbal Romero , Sebastián Ventura , Paul De Bra, Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors, User Modeling and User-Adapted Interaction, v.14 n.5, p.425-464, January 2005
	Szymon Jaroszewicz , Tobias Scheffer , Dan A. Simovici, Scalable pattern mining with Bayesian networks as background knowledge, Data Mining and Knowledge Discovery, v.18 n.1, p.56-100, February 2009
	Karthik Rajasethupathy , Anthony Scime , Kulathur S. Rajasethupathy , Gregg R. Murray, Finding "persistent rules": Combining association and classification results, Expert Systems with Applications: An International Journal, v.36 n.3, p.6019-6024, April, 2009
	Ling Zhou , Stephen Yau, Association rule and quantitative association rule mining among infrequent items, Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007), p.1-9, August 12-12, 2007, San Jose, California
	Ling Zhou , Stephen Yau, Efficient association rule mining among both frequent and infrequent items, Computers & Mathematics with Applications, v.54 n.6, p.737-749, September, 2007
	Pawel Terlecki , Krzysztof Walczak, Jumping emerging patterns with negation in transaction databases - Classification and discovery, Information Sciences: an International Journal, v.177 n.24, p.5675-5690, December, 2007
	Gianluigi Greco , Antonella Guzzo , Luigi Pontieri, Mining taxonomies of process models, Data & Knowledge Engineering, v.67 n.1, p.74-102, October, 2008
	Dong (Haoyuan) Li , Anne Laurent , Pascal Poncelet, Recognizing unexpected recurrence behaviors with fuzzy measures in sequence databases, Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology, October 28-31, 2008, Cergy-Pontoise, France
	Rana Malhas , Zaher Al Aghbari, Interestingness filtering engine: Mining Bayesian networks for interesting patterns, Expert Systems with Applications: An International Journal, v.36 n.3, p.5137-5145, April, 2009
	Mojtaba Koopaei , Behrouz Minaei-Bidgoli, A new method for ranking changes in customer's behavioral patterns in department stores, Proceedings of the 11th International Conference on Electronic Commerce, August 12-15, 2009, Taipei, Taiwan
	Malik Agyemang , Ken Barker , Rada Alhajj, A comprehensive survey of numeric and symbolic outlier mining techniques, Intelligent Data Analysis, v.10 n.6, p.521-538, December 2006