Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

Hang, X., Dai, Honghua and Lanham, E. 2003, Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data, in Design and Application of Hybrid Intelligent Systems : Proceedings of the 2003 International Conference on Hybrid Intelligent Systems, IOS Press, Amsterdam, Netherlands, pp. 702-711.

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Title Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data
Author(s) Hang, X.
Dai, Honghua
Lanham, E.
Conference name Hybrid Intelligent Systems. Conference (3rd : 2003 : Melbourne, Victoria)
Conference location Melbourne, Victoria
Conference dates 14-17 Dec. 2003
Title of proceedings Design and Application of Hybrid Intelligent Systems : Proceedings of the 2003 International Conference on Hybrid Intelligent Systems
Editor(s) Abraham, Ajith and
Koppen, Mario
Franke, Katrin
Publication date 2003
Conference series Hybrid Intelligent Systems Conference
Start page 702
End page 711
Publisher IOS Press
Place of publication Amsterdam, Netherlands
Summary The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.

ISBN 1586033948
9781586033941
Language eng
Field of Research 080105 Expert Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005089

Document type: Conference Paper
Collection: School of Information Technology
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