Abstract
We consider the problem of finding frequent subsequences in sequential data. We examine three algorithms using a trie with K levels. The O(K 2 n) breadth-first (BF) algorithm inserts a pattern into the trie at level k only if level k-1 has been completed. The O(Kn) depth-first (DF) algorithm inserts a pattern and all its prefixes into the trie before examining another pattern. A threshold is used to store only frequent subsequences. Since DF cannot apply the threshold until the trie is complete, it makes poor use of memory. The heuristic depth-first (HDF) algorithm, a variant of DF, uses the threshold in the same manner as BF. HDF gains efficiency but loses a predictable amount of accuracy.
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
Agrawal, R., and Srikant, R.,“Mining Sequential Patterns.” Proceedings IEEE International Conference on Data Engineering, Taipei, Taiwan, 1995.
Hand, D. J., Mannila, H., and Smyth, P., Principles of Data Mining, MIT Press, Cambridge, Massachusetts, 2001.
Jiang, L., “A Quick Look at Methods for Mining Long Subsequences”, Proceedings AI’2003, this volume.
Srikant, R., and Agrawal, R., Mining Sequential Patterns: Generalizations and Performance Improvements. Research Report RJ9994, IBM Almaden Research Center, San Jose, California, December 1995.
Vilo, J., Discovering Frequent Patterns from Strings, Technical Report C-1998-9, Department of Computer Science, University of Helsinki, FIN-00014, University of Helsinki, May 1998.
Zaki, M. J., Fast Mining of Sequential Patterns in Very Large Databases, Technical Report 668, Computer Science Department, University of Rochester, Rochester, New York, Nov. 1999
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
Jiang, L., Hamilton, H.J. (2003). Methods for Mining Frequent Sequential Patterns. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_38
Download citation
DOI: https://doi.org/10.1007/3-540-44886-1_38
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40300-5
Online ISBN: 978-3-540-44886-0
eBook Packages: Springer Book Archive