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Melbourne, Florida
Nov. 19, 2003 to Nov. 22, 2003
ISBN: 0-7695-1978-4
pp: 347
Petre Tzvetkov , University of Illinois at Urbana-Champaign, Illinois
Xifeng Yan , University of Illinois at Urbana-Champaign, Illinois
Jiawei Han , University of Illinois at Urbana-Champaign, Illinois
ABSTRACT
Sequential pattern mining has been studied extensively in data mining community. Most previous studies require the specification of a minimum support threshold to perform the mining. However, it is difficult for users to provide an appropriate threshold in practice. To overcome this difficulty, we propose an alternative task: mining top-k frequent closed sequential patterns of length no less than min_l, where k is the desired number of closed sequential patterns to be mined, and min_l is the minimum length of each pattern. We mine closed patterns since they are compact representations of frequent patterns.<div></div> We developed an efficient algorithm, called TSP, which makes use of the length constraint and the properties of top-k closed sequential patterns to perform dynamic support-raising and projected database-pruning. Our extensive performance study shows that TSP outperforms the closed sequential pattern mining algorithm even when the latter is running with the best tuned minimum support threshold.
INDEX TERMS
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CITATION
Petre Tzvetkov, Xifeng Yan, Jiawei Han, "TSP: Mining Top-K Closed Sequential Patterns", ICDM, 2003, 2013 IEEE 13th International Conference on Data Mining, 2013 IEEE 13th International Conference on Data Mining 2003, pp. 347, doi:10.1109/ICDM.2003.1250939
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