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A Database-Reduction-Based Algorithm for Episode Mining
Taipei, Taiwan December 04-December 07
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PDCAT.2006.3Seventh International Conference on P ...
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Yunlan Wang, Xingshe Zhou, Peiqi Liu, "A Database-Reduction-Based Algorithm for Episode Mining," Parallel and Distributed Computing Applications and Technologies, International Conference on, pp. 123-127, Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06), 2006.
 
 
BibTex
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@article{ 10.1109/PDCAT.2006.3,
author = {Yunlan Wang and Xingshe Zhou and Peiqi Liu},
title = {A Database-Reduction-Based Algorithm for Episode Mining},
journal ={Parallel and Distributed Computing Applications and Technologies, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2736-1},
pages = {123-127},
doi = {http://doi.ieeecomputersociety.org/10.1109/PDCAT.2006.3},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Parallel and Distributed Computing Applications and Technologies, International Conference on
TI - A Database-Reduction-Based Algorithm for Episode Mining
SN - 0-7695-2736-1
SP123
EP127
A1 - Yunlan Wang,
A1 - Xingshe Zhou,
A1 - Peiqi Liu,
PY - 2006
KW - null
VL - 0
JA - Parallel and Distributed Computing Applications and Technologies, International Conference on
ER -
 
Yunlan Wang, Northwestern Polytechnical University, China
Xingshe Zhou, Northwestern Polytechnical University, China
Peiqi Liu, Xi'an University of Architecture and Technology, China
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.
Citation:
Yunlan Wang, Xingshe Zhou, Peiqi Liu, "A Database-Reduction-Based Algorithm for Episode Mining," pdcat, pp.123-127, Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'06), 2006
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