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Mining Sequential Patterns across Time Sequences

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Abstract

In this paper, we deal with mining sequential patterns in multiple time sequences. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILE (MIning in muLtiple sEquences), an efficient algorithm to facilitate the mining process. MILE recursively utilizes the knowledge of existing patterns to avoid redundant data scanning, and therefore can effectively speed up the new patterns’ discovery process. Another unique feature of MILE is that it can incorporate prior knowledge of the data distribution in time sequences into the mining process to further improve the performance. Extensive empirical results show that MILE is significantly faster than PrefixSpan. As MILE consumes more memory than PrefixSpan, we also present a solution to trade time efficiency in memory constrained environments.

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Authors and Affiliations

  1. Department of Statistics, University of California, Los Angeles, 8951 Mathematical Sciences Building, Los Angeles, CA, 90095, USA

    Gong Chen

  2. Department of Computer Science, University of Vermont, 33 Colchester Avenue, Burlington, VT, 05405, USA

    Xindong Wu

  3. Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL, 33431, USA

    Xingquan Zhu

Authors
  1. Gong Chen
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  2. Xindong Wu
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  3. Xingquan Zhu
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Corresponding author

Correspondence to Gong Chen.

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Chen, G., Wu, X. & Zhu, X. Mining Sequential Patterns across Time Sequences. New Gener. Comput. 26, 75–96 (2007). https://doi.org/10.1007/s00354-007-0036-2

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  • DOI: https://doi.org/10.1007/s00354-007-0036-2

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