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Title: Finding constrained frequent episodes using minimal occurrences
Authors: Ma, X.
Pang, H.
Tan, K.-L. 
Issue Date: 2004
Citation: 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.
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
ISBN: 0769521428
DOI: 10.1142/9789812702289_0048
Appears in Collections:Staff Publications

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