SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
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In this paper, we systematically explore the search 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 [2] and present the improved algorithm, SPAMSEPIPE, which uses SEP and IEP to prune the search space by sharing the frequent 2-sequences lists. A set of comprehensive performance experiments study shows that SPAMSEPIEP outperforms SPAM by a factor of 10 on small datasets and better than 30% to 50% on reasonably large dataset.