Publication Type

Conference Proceeding Article

Publication Date

11-2004

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 (Mannila and Toivonen, 1996).

Keywords

constrained frequent episode, episode mining, episode prefix tree, minimal occurrences, position pairs set, prefix-growth approach

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Fourth IEEE International Conference on Data Mining: ICDM 2004: Proceedings: 1-4 November, 2004, Brighton, United Kingdom

First Page

471

Last Page

474

ISBN

9780769521428

Identifier

10.1109/ICDM.2004.10043

Publisher

IEEE

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10043

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