Efficient mining of max frequent patterns in a generalized environment

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Efficient mining of max frequent patterns in a generalized environment

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Conference on Information and Knowledge Management archive
Proceedings of the 15th ACM international conference on Information and knowledge management table of contents

Arlington, Virginia, USA

POSTER SESSION: Posters table of contents

Pages: 810 - 811

Year of Publication: 2006

ISBN:1-59593-433-2

Authors

Daniel Kunkle	Northeastern University, Boston, MA
Donghui Zhang	Northeastern University, Boston, MA
Gene Cooperman	Northeastern University, Boston, MA

Sponsors

ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

ACM New York, NY, USA

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ABSTRACT

This poster paper summarizes our solution for mining max frequent generalized itemsets (g-itemsets), a compact representation for frequent patterns in the generalized environment.

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	Roberto J. Bayardo, Jr., Efficiently mining long patterns from databases, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.85-93, June 01-04, 1998, Seattle, Washington, United States
	2	Ramakrishnan Srikant , Rakesh Agrawal, Mining Generalized Association Rules, Proceedings of the 21th International Conference on Very Large Data Bases, p.407-419, September 11-15, 1995
	3	K. Sriphaew and T. Theeramunkong. Fast Algorithms for Mining Generalized Frequent Patterns of Generalized Association Rules. IEICE Transactions on Information and Systems, E87-D(3), March 2004.