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Mining Top-K Sequential Rules

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Advanced Data Mining and Applications (ADMA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7121))

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Abstract

Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine-tuning the parameters can be very time-consuming. In this paper, we address this problem by proposing TopSeqRules, an efficient algorithm for mining the top-k sequential rules from sequence databases, where k is the number of sequential rules to be found and is set by the user. Experimental results on real-life datasets show that the algorithm has excellent performance and scalability.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan

    Philippe Fournier-Viger & Vincent S. Tseng

Authors
  1. Philippe Fournier-Viger
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  2. Vincent S. Tseng
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, 100084, Beijing, China

    Jie Tang  & Jianyong Wang  & 

  2. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China

    Irwin King

  3. Faculty of Engineering and Information Technology, University of Technology, 2007, Sydney, NSW, Australia

    Ling Chen

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Fournier-Viger, P., Tseng, V.S. (2011). Mining Top-K Sequential Rules. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_14

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  • DOI: https://doi.org/10.1007/978-3-642-25856-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25855-8

  • Online ISBN: 978-3-642-25856-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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