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PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
Abstract
Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of \t which may substantially reduce the number of combinations to be examined. However, \t still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. In this paper, we propose a novel sequential pattern mining method, called PrefixSpan (i.e., Prefix-projected Sequential pattern mining), which explores prefixprojection in sequential pattern mining. PrefixSpan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover, prefix-projection substantially reduces the size of projected databases and leads to efficient processing. Our performance study shows that PrefixSpan outperforms both the -based GSP algorithm and another recently proposed method, FreeSpan, in mining large sequence databases. 1
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Cites
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Self-citation
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Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
Self-citationTsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818
Activity recognition on streaming sensor data
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Metadata
- Authors
- Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., & Hsu, M.-C.
- Venue
- Proceedings 17th International Conference on Data Engineering
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-29
- Pages
- 215-224
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2147694185
- Cited by
- 1,791
- References
- 1
- FWCI
- 92.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., & Hsu, M.-C. (2005). PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth. Proceedings 17th International Conference on Data Engineering. https://doi.org/10.1109/icde.2001.914830
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