Abstract
We present MinMax, a new algorithm for mining maximal frequent itemsets(MFI) from a transaction database. It is based on depth-first traversal and iterative. It combines a vertical tidset representation of the database with effective pruning mechanisms. MinMax removes all the non-maximal frequent itemsets to get the exact set of MFI directly, needless to enumerate all the frequent itemsets from smaller ones step by step. It backtracks to the proper ancestor directly, needless level by level. We found MinMax to be more effective than GenMax, a state-of-the-art algorithm for finding maximal frequent itemsets, to prune the search space to get the exact set of MFI.
This paper is supported by the National Natural Science Foundation of China under Grant No.60273075
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
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A.I. Verkamo: Fast Discovery of Association Rules. Advances in Knowledge Discovery and Data Mining, Chapter 12, AAAI/MIT Press, 1995.
R. Agrawal, C. Aggarwal, and V. Prasad. Depth First Generation of Long Patterns. In ACM SIGKDD Conf., Aug. 2000.
R. J. Bayardo. Efficiently mining long patterns from databases. In ACM SIGMOD Conf., June 1998.
D. Burdick, M. Calimlim, and J. Gehrke. MAFIA: a maximal frequent itemset algorithm for transactional databases. In Intl. Conf. on Data Engineering, Apr. 2001.
K. Gouda, M. J. Zaki. Efficiently Mining Maximal Frequent Itemsets. In 1st IEEE Intl. Conf. On data mining, Nov. 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Li, Q., Ma, C., Li, K. (2003). A Maximal Frequent Itemset Algorithm. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_82
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_82
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive