Title :
Mining Maximal Sequential Patterns
Author :
Guan, En-Zheng ; Chang, Xiao-Yu ; Wang, Zhe ; Zhou, Chun-Guang
Author_Institution :
Coll. of Comput. Sci., Jilin Univ., Changchun
Abstract :
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
Keywords :
data mining; database management systems; sequences; maximal frequent sequential pattern mining algorithm; repeated information; sequence databases; Computer science; Computer science education; Data mining; Educational institutions; Electronic mail; Itemsets; Knowledge engineering; Laboratories; Testing; Transaction databases;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614668