Nov. 19, 2003 to Nov. 22, 2003
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
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.
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