Title :
A Database-Reduction-Based Algorithm for Episode Mining
Author :
Wang, Yunlan ; Zhou, Xingshe ; Liu, Peiqi
Author_Institution :
Center for High Performance Comput., Northwestern Polytech. Univ., Xi´´an
Abstract :
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
Keywords :
data mining; data reduction; WINEPI algorithm; database-reduction-based algorithm; episode mining; knowledge discovery; Application software; Association rules; Computer networks; Concurrent computing; Control engineering; Data mining; Distributed computing; High performance computing; Tellurium; Transaction databases;
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies, 2006. PDCAT '06. Seventh International Conference on
Conference_Location :
Taipei
Print_ISBN :
0-7695-2736-1
DOI :
10.1109/PDCAT.2006.3
