Fast mining of non-derivable episode rules in complex sequences

Gan, Min and Dai, Honghua 2011, Fast mining of non-derivable episode rules in complex sequences. In Torra, Vicenç, Narukawa, Yasuo, Yin, Jianping and Long, Jun (ed), Modeling decisions for artificial intelligence, Springer, Berlin, Germany, pp.67-78.

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Title Fast mining of non-derivable episode rules in complex sequences
Author(s) Gan, Min
Dai, HonghuaORCID iD for Dai, Honghua
Title of book Modeling decisions for artificial intelligence
Editor(s) Torra, Vicenç
Narukawa, Yasuo
Yin, Jianping
Long, Jun
Publication date 2011
Series Lecture notes in artificial intelligence; v. 6820
Chapter number 8
Total chapters 23
Start page 67
End page 78
Total pages 12
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) episode rules
complex sequences
sequence data mining
Summary Researchers have been endeavoring to discover concise sets of episode rules instead of complete sets in sequences. Existing approaches, however, are not able to process complex sequences and can not guarantee the accuracy of resulting sets due to the violation of anti-monotonicity of the frequency metric. In some real applications, episode rules need to be extracted from complex sequences in which multiple items may appear in a time slot. This paper investigates the discovery of concise episode rules in complex sequences. We define a concise representation called non-derivable episode rules and formularize the mining problem. Adopting a novel anti-monotonic frequency metric, we then develop a fast approach to discover non-derivable episode rules in complex sequences. Experimental results demonstrate that the utility of the proposed approach substantially reduces the number of rules and achieves fast processing.
ISBN 3642225896
ISSN 0302-9743
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890202 Application Tools and System Utilities
HERDC Research category B1 Book chapter
Copyright notice ©2011, Springer-Verlag Berlin Heidelberg
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Collection: School of Information Technology
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