TY - GEN
T1 - A database-reduction-based algorithm for episode mining
AU - Wang, Yunlan
AU - Zhou, Xingshe
AU - Liu, Peiqi
PY - 2006
Y1 - 2006
N2 - Event Sequence arises naturally in many applications. Episode mining can discovery the knowledge hidden in the event sequence. Currently, the most influential algorithm for episode mining is WINEPI. However, it is likely to suffer from the tendency of generating too many of candidate episodes. In this paper, a novel algorithm named DRE for mining frequent episodes is presented. It studied the conditions for the events which can be pruned from the database, so the size of database is reduced gradually. The performance of algorithm DRE was evaluated and compared with WINEPI algorithm. The results demonstrate that the DRE has better performance.
AB - Event Sequence arises naturally in many applications. Episode mining can discovery the knowledge hidden in the event sequence. Currently, the most influential algorithm for episode mining is WINEPI. However, it is likely to suffer from the tendency of generating too many of candidate episodes. In this paper, a novel algorithm named DRE for mining frequent episodes is presented. It studied the conditions for the events which can be pruned from the database, so the size of database is reduced gradually. The performance of algorithm DRE was evaluated and compared with WINEPI algorithm. The results demonstrate that the DRE has better performance.
UR - https://www.scopus.com/pages/publications/38949217355
U2 - 10.1109/PDCAT.2006.3
DO - 10.1109/PDCAT.2006.3
M3 - 会议稿件
AN - SCOPUS:38949217355
SN - 0769527361
SN - 9780769527369
T3 - Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
SP - 123
EP - 127
BT - Proceedings - Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006
T2 - 7th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2006
Y2 - 4 December 2006 through 7 December 2006
ER -