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Canadian Conference on Artificial Intelligence

Canadian AI 2012: Advances in Artificial Intelligence pp 299–304Cite as

Mining Sequential Rules Common to Several Sequences with the Window Size Constraint

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7310))

Abstract

We present an algorithm for mining sequential rules common to several sequences, such that rules have to appear within a maximum time span. Experimental results with real-life datasets show that the algorithm can reduce the execution time, memory usage and the number of rules generated by several orders of magnitude compared to previous algorithms.

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References

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Author information

Authors and Affiliations

  1. Dept. of Computer Sciences, University of Moncton, Canada

    Philippe Fournier-Viger

  2. Dep. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan

    Cheng-Wei Wu & Vincent S. Tseng

  3. Dept. of Computer Sciences, University of Quebec in Montreal, Canada

    Roger Nkambou

Authors
  1. Philippe Fournier-Viger
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  2. Cheng-Wei Wu
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  3. Vincent S. Tseng
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  4. Roger Nkambou
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Computer Science, Department of Computer Science and Software Engineering, Concordia University, H3G 1M8, Montreal, QC, Canada

    Leila Kosseim

  2. Faculty of Engineering, School of Electrical Engineering and Computer Science, University of Ottawa, K1N 6N5, Ottawa, ON, Canada

    Diana Inkpen

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© 2012 Springer-Verlag Berlin Heidelberg

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Fournier-Viger, P., Wu, CW., Tseng, V.S., Nkambou, R. (2012). Mining Sequential Rules Common to Several Sequences with the Window Size Constraint. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_27

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  • DOI: https://doi.org/10.1007/978-3-642-30353-1_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30352-4

  • Online ISBN: 978-3-642-30353-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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