In this work, we study the problem of closed sequential pattern mining. We propose a novel approach which extends a frequent sequence with closed itemsets instead of single items. The motivation is that closed sequential patterns are composed of only closed itemsets. Hence, unnecessary item extensions which generates non-closed sequential patterns can be avoided. Experimental evaluation shows that the proposed approach is two orders of magnitude faster than previous works with a modest memory cost. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Huang, K. Y., Chang, C. H., Tung, J. H., & Ho, C. T. (2006). COBRA: Closed sequential pattern mining using Bi-phase reduction approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4081 LNCS, pp. 280–291). Springer Verlag. https://doi.org/10.1007/11823728_27
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