Effective pruning strategies for sequential pattern mining
dc.contributor.author | Xu, Y. | |
dc.contributor.author | Ma, Z. | |
dc.contributor.author | Li, L. | |
dc.contributor.author | Dillon, Tharam S. | |
dc.contributor.editor | Q. Luo | |
dc.contributor.editor | M. Gong | |
dc.contributor.editor | F. Xiong | |
dc.contributor.editor | F. Yu | |
dc.date.accessioned | 2017-01-30T13:41:39Z | |
dc.date.available | 2017-01-30T13:41:39Z | |
dc.date.created | 2009-03-01T18:01:43Z | |
dc.date.issued | 2008 | |
dc.identifier.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. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/34160 | |
dc.identifier.doi | 10.1109/WKDD.2008.22 | |
dc.description.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. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) Computer Society | |
dc.title | Effective pruning strategies for sequential pattern mining | |
dc.type | Conference Paper | |
dcterms.source.startPage | 21 | |
dcterms.source.endPage | 24 | |
dcterms.source.title | Proceedings of the international workshop on knowledge discovery and data mining (WKDD 2008) | |
dcterms.source.series | Proceedings of the international workshop on knowledge discovery and data mining (WKDD 2008) | |
dcterms.source.isbn | 9789639799196 | |
dcterms.source.conference | International Workshop on Knowledge Discovery and Data Mining (WKDD 2008) | |
dcterms.source.conference-start-date | 23 Jan 2008 | |
dcterms.source.conferencelocation | Adelaide, Australia | |
dcterms.source.place | Belgium | |
curtin.note |
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curtin.department | Centre for Extended Enterprises and Business Intelligence | |
curtin.accessStatus | Open access | |
curtin.faculty | Curtin Business School | |
curtin.faculty | School of Information Systems |