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Effective pruning strategies for sequential pattern mining

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Open access
Authors
Xu, Y.
Ma, Z.
Li, L.
Dillon, Tharam S.
Date
2008
Type
Conference Paper

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Citation
Xu, Yusheng and Ma, Zhixin and Li, Lian and Dillon, Tharam. 2008. Effective pruning strategies for sequential pattern mining, in Luo, Q. and Gong, M. and Xiong, F. and Yu, F. (ed), International Workshop on Knowledge Discovery and Data Mining, Jan 23 2008, pp. 21-24. Adelaide, Australia: Institute of Electrical and Electronics Engineers (IEEE) Computer Society.
Source Title
Proceedings of the international workshop on knowledge discovery and data mining (WKDD 2008)
Source Conference
International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)
DOI
ISBN
Faculty
Curtin Business School
School of Information Systems
School
Centre for Extended Enterprises and Business Intelligence
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Copyright © 2008. IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder

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Abstract

In this paper, we systematically explore the search space of frequent sequence mining and present two novel pruning strategies, S E P (Sequence Extension Pruning) and I EP (Item Extension Pruning), which can be used in all Aption-like sequence mining algorithms or lattice-theoretic approaches. With a little more memory overhead, proposed pruning strategies can prune invalidated search space and decrease the total cost of frequency counting effectively. For effectiveness testing reason, we optimize SPAM [2) and present the improved algorithm, S P AMSEPIEP' which uses S E P and IEP to prune the search space by sharing the frequent 2sequences lists. A set of comprehensive performance experiments study shows that S P AMSEPIEP outperforms SPAM by a factor of 10 on small datasets and better than 30 % to 50 % on reasonably large dataset.