Mining Maximal Sequential Patterns
En-Zheng Guan, Xiaoyu Chang, Zhe Wang, Chunguang Zhou · 2006
To solve the problem that when patterns are long, frequent sequential patterns mining may generate an exponential number of results, which often makes decision-makers perplexed for there is too much useless repeated information, a novel algorithm MFSPAN (Maximal Frequent Sequential Pattern mining algorithm) to mine the complete set of maximal frequent sequential patterns in sequence databases is proposed. MFSPAN takes full advantage of the property that two different sequences may share a common prefix to reduce itemset comparing times. Experiments on standard test data show that MFSPAN is very effective.