DocumentCode
2927271
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
fYear
2006
fDate
Dec. 2006
Firstpage
123
Lastpage
127
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/PDCAT.2006.3
Filename
4032163
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