Article

Finding frequent itemsets by transaction mapping

Authors:

Mingjun Song,

Sanguthevar RajasekaranAuthors Info & Claims

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing

Pages 488 - 492

https://doi.org/10.1145/1066677.1066789

Published: 13 March 2005 Publication History

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Abstract

In this paper, we present a novel algorithm for mining complete frequent itemsets. This algorithm is referred to as the TM algorithm from hereon. In this algorithm, we employ the vertical representation of a database. Transaction ids of each itemset are mapped and compressed to continuous transaction intervals in a different space thus reducing the number of intersections. When the compression coefficient becomes smaller than the average number of comparisons for intervals intersection, the algorithm switches to transaction id intersection. We have evaluated the algorithm against two popular frequent itemset mining algorithms -- FP-growth and dEclat using a variety of data sets with short and long frequent patterns. Experimental data show that the TM algorithm outperforms these two algorithms.

References

[1]

Agrawal, R, Imielinski, T. and Swami, A. N. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD International Conference on Management of Data, ACM Press, Washington DC, May 1993, 207--216.

Digital Library

Google Scholar

[2]

Agrawal, R. and Srikant, R. Fast algorithms for mining association rules. In Proceedings of 20th International Conference on Very Large Data Bases, Morgan Kaufmann, 1994, 487--499.

Digital Library

Google Scholar

[3]

Park, J. S., Chen, M.-S., and Yu, P. S. An effective hash based algorithm for mining association rules. In Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, ACM Press, San Jose, California, May 1995, 175--186.

Digital Library

Google Scholar

[4]

Brin, S., Motwani, R, Ullman, J. D., and Tsur, S. Dynamic itemset counting and implication rules for market basket data. In Proceedings of ACM SIGMOD International Conference on Management of Data, ACM Press, Tucson, Arizona, May 1997, 255--264.

Digital Library

Google Scholar

[5]

Savasere, A., Omiecinski, E., and Navathe, S. An efficient algorithm for mining association rules in large databases. In Proceedings of 21th International Conference on Very Large Data Bases. Morgan Kaufmann, 1995, 432--444.

Digital Library

Google Scholar

[6]

Han, J., Pei, J., and Yin, Y. Mining frequent patterns without candidate generation. In Procedings of ACM SIGMOD Intnational Conference on Management of Data, ACM Press, Dallas, Texas, May 2000, 1--12.

Digital Library

Google Scholar

[7]

Zaki, M. J., Parthasarathy, S., Ogihara, M., and Li, W. New algorithms for fast discovery of association rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, 1997, 283--286.

Digital Library

Google Scholar

[8]

Shenoy, P., Haritsa, J. R., Sudarshan, S., Bhalotia, G., Bawa, M., and Shah, D. Turbo-charging vertical mining of large databases. In Procedings of ACM SIGMOD Intnational Conference on Management of Data, ACM Press, Dallas, Texas, May 2000, 22--23.

Digital Library

Google Scholar

[9]

Burdick, D., Calimlim, M., and Gehrke, J. MAFIA: a maximal frequent itemset algorithm for transactional databases. In Proceedings of International Conference on Data Engineering, Heidelberg, Germany, April 2001, 443--452.

Digital Library

Google Scholar

[10]

Zaki, M. J., and Gouda, K. Fast vertical mining using diffsets. In Proceedings of the Nineth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, D. C., ACM Press, New York, 2003, 326--335.

Digital Library

Google Scholar

[11]

Agrawal, R, Aggarwal, C., and Prasad, V. A Tree Projection Algorithm for Generation of Frequent Item Sets, Parallel and Distributed Computing, 2000, 350--371.

Digital Library

Google Scholar

[12]

Bayardo, R. J. Efficiently mining long patterns from databases. In Procedings of ACM SIGMOD Intnational Conference on Management of Data, ACM Press, Seattle, Washington, June 1998, 85--93.

Digital Library

Google Scholar

Cited By

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(2022)An Enhanced Fast – High Utility Item set Mining Method for Large DatasetsData Analytics and Artificial Intelligence10.46632/daai/2/1/102:1(59-63)Online publication date: 1-May-2022
https://doi.org/10.46632/daai/2/1/10
Bo L(2008)Conjunction Graph-Based Frequent-Sets Fast Discovering AlgorithmProceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 0310.1109/IITA.2008.117(19-23)Online publication date: 20-Dec-2008
https://dl.acm.org/doi/10.1109/IITA.2008.117
Yu KWu B(2008)A High Performance Frequent Itemset Mining Algorithm Using Confidence Frequent Pattern TreeProceedings of the 2008 3rd International Conference on Innovative Computing Information and Control10.1109/ICICIC.2008.36Online publication date: 18-Jun-2008
https://dl.acm.org/doi/10.1109/ICICIC.2008.36

Index Terms

Finding frequent itemsets by transaction mapping
1. Information systems
  1. Information systems applications

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Published In

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing

March 2005

1814 pages

ISBN:1581139640

DOI:10.1145/1066677

Conference Chair:
Hisham M. Haddad
Kennesaw State University
,
Editor:
Lorie M. Liebrock
New Mexico Institute of Mining and Technology, Socorro, NM
,
Program Chairs:
Andrea Omicini
Alma Mater Studiorum, Universita di Bologna, Italy
,
Roger L. Wainwright
Univerity of Tulsa, OK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2005

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SIGAPP

SAC05: The 2005 ACM Symposium on Applied Computing

March 13 - 17, 2005

New Mexico, Santa Fe

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all

(2022)An Enhanced Fast – High Utility Item set Mining Method for Large DatasetsData Analytics and Artificial Intelligence10.46632/daai/2/1/102:1(59-63)Online publication date: 1-May-2022
https://doi.org/10.46632/daai/2/1/10
Bo L(2008)Conjunction Graph-Based Frequent-Sets Fast Discovering AlgorithmProceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 0310.1109/IITA.2008.117(19-23)Online publication date: 20-Dec-2008
https://dl.acm.org/doi/10.1109/IITA.2008.117
Yu KWu B(2008)A High Performance Frequent Itemset Mining Algorithm Using Confidence Frequent Pattern TreeProceedings of the 2008 3rd International Conference on Innovative Computing Information and Control10.1109/ICICIC.2008.36Online publication date: 18-Jun-2008
https://dl.acm.org/doi/10.1109/ICICIC.2008.36

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A Transaction Mapping Algorithm for Frequent Itemsets Mining

Finding Frequent Closed Itemsets in Sliding Window in Linear Time

An adaptive approach to mining frequent itemsets efficiently