Hyung Geun Kim , Whan Kyu Whang

The KIPS Transactions:PartD, Vol. 12, No. 6, pp. 807-816, Dec. 2005
10.3745/KIPSTD.2005.12.6.807,   PDF Download:

Abstract

Sequential pattern mining finds all of the frequent sequences satisfying a minimum support threshold in a large database. However, when mining long frequent sequences, or when using very low support thresholds, the performance of currently reported algorithms often degrades dramatically. In this paper, we propose a novel sequential pattern algorithm using only closed frequent sequences which are small subset of very large frequent sequences. Our algorithm generates the candidate sequences by depth-first search strategy in order to effectively prune. Using bitmap representation of underlying databases, we can effectively calculate supports in terms of bit operations and prune sequences in much less time. Performance study shows that our algorithm outperforms the previous algorithms.

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