Abstract
This paper studies the problem of mining frequent sequences in transactional databases. In [1], Agrawal and Srikant proposed the AprioriAll algorithm for extracting frequently occurring sequences. AprioriAll is an iterative algorithm. It scans the database a number of times depending on the length of the longest frequent sequences in the database. The I/O cost is thus substantial if the database contains very long frequent sequences. In this paper, we propose a new I/O-efficient algorithm FFS. Experiment results show that FFS saves I/O cost significantly compared with AprioriAll. The I/O saving is obtained at a cost of a mild overhead in CPU cost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rakesh Agrawal and Ramakrishnan Srikant. Mining sequential patterns. In Proc. of the 11th Int'l Conference on Data Engineering, Taipei, Taiwan, March 1995.
Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, and Shalom Tsur. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the ACM SIGMOD Conference on Management of Data, 1997.
Ming-Syan Chen, Jong Soo Park, and Philip S. Yu. Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering, 10(2), March/April 1998.
Minos N. Garofalakis, Rajeev Rastogi, and Kyuseok Shim. SPIRIT: Sequential pattern mining with regular expression constraints. In Proceedings of the 25th International Conference on Very Large Data Bases, Edinburgh, Scotland, UK, September 1999.
Dao-I Lin and Zvi M. Kedem. Pincer-search: A new algorithm for discovering the maximum frequent set. In Proceedings of the 6th Conference on Extending Database Technology (EDBT), Valencia, Spain, March 1998.
K.K. Loo, C.L. Yip, Ben Kao, and David Cheung. Exploiting the duality of maximal frequent itemsets and minimal infrequent itemsets for I/O efficient association rule mining. In Proc. of the 11th International Conference on Database and Expert Systems Conference, London, Sept. 2000.
S. Parthasarathy, M.J. Zaki, M. Ogihara, and S. Dwarkadas. Incremental and interactive sequence mining. In Proceedings of the 1999 ACM 8th International Conference on Information and Knowledge Management (CIKM’99), Kansas City, MO USA, November 1999.
T. Imielinski R. Agrawal and A. Swami. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD International Conference on Management of Data, page 207, Washington, D.C., May 1993.
Ramakrishnan Srikant and Rakesh Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proc. of the 5th Conference on Extending Database Technology (EDBT), Avignion, France, March 1996.
Mohammed J. Zaki. Efficient enumeration of frequent sequences. In Proceedings of the 1998 ACM 7th International Conference on Information and Knowledge Management(CIKM’98), Washington, United States, November 1998.
Minghua Zhang, Ben Kao, C.L. Yip, and David Cheung. FFS-An I/O efficient algorithm for mining frequent sequences. Technical Report CS Technical Report TR-2000-6, University of Hong Kong, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, M., Kao, B., Yip, CL., Cheung, D. (2001). FFS - An I/O-Efficient Algorithm for Mining Frequent Sequences. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_32
Download citation
DOI: https://doi.org/10.1007/3-540-45357-1_32
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41910-5
Online ISBN: 978-3-540-45357-4
eBook Packages: Springer Book Archive