International Workshop on Knowledge Discovery and Data Mining (2008)
Jan. 23, 2008 to Jan. 24, 2008
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WKDD.2008.22
space of frequent sequence mining and present two novel pruning strategies, SEP (Sequence Extension Pruning) and IEP (Item Extension Pruning), which can be used in all Apriori-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  and present the improved algorithm, SP AMSEPIEP, which uses SEP and IEP to prune the search space by sharing the frequent 2sequences lists. A set of comprehensive performance experiments study shows that SP AMSEPIEP outperforms SPAM by a factor of 10 on small datasets and better than 30% to 50% on reasonably large dataset.
T. S. Dillon, L. Lian, M. Zhixin and X. Yusheng, "Effective Pruning Strategies for Sequential Pattern Mining," International Workshop on Knowledge Discovery and Data Mining(WKDD), Adelaide, Australia, 2008, pp. 21-24.