Please use this identifier to cite or link to this item: https://doi.org/10.1142/9789812702289_0048
Title: Finding constrained frequent episodes using minimal occurrences
Authors: Ma, X.
Pang, H.
Tan, K.-L. 
Issue Date: 2004
Source: Ma, X., Pang, H., Tan, K.-L. (2004). Finding constrained frequent episodes using minimal occurrences. Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 : 471-474. ScholarBank@NUS Repository. https://doi.org/10.1142/9789812702289_0048
Abstract: Recurrent combinations of events within an event sequence, known as episodes, often reveal useful information. Most of the proposed episode mining algorithms adopt an apriori-like approach that generates candidates and then calculates their support levels. Obviously, such an approach is computationally expensive. Moreover, those algorithms are capable of handling only a limited range of constraints. In this paper, we introduce two mining algorithms - Episode Prefix Tree (EPT) and Position Pairs Set (PPS) - based on a prefix-growth approach to overcome the above limitations. Both algorithms push constraints systematically into the mining process. Performance study shows that the proposed algorithms run considerably faster than MINEPI [4]. © 2004 IEEE.
Source Title: Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
URI: http://scholarbank.nus.edu.sg/handle/10635/41485
ISBN: 0769521428
DOI: 10.1142/9789812702289_0048
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

9
checked on Dec 14, 2017

Page view(s)

50
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.