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TSPIN: mining top-k stable periodic patterns

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Abstract

Discovering periodic patterns consists of identifying all sets of items (values) that periodically co-occur in a discrete sequence. Although traditional periodic pattern mining algorithms have multiple applications, they have two key limitations. First, they consider that a pattern is not periodic if the time difference between two of its successive occurrences is greater than a maxPer threshold. But this constraint is too strict, as a pattern may be discarded based on only two of its occurrences, although it may be usually periodic. Second, traditional algorithms use a constraint that the support (occurrence frequency) of a pattern must be no less than a minSup threshold. But setting that parameter is not intuitive. Hence, it is usually set by trial and error, which is time-consuming. This paper addresses the first limitation by introducing a concept of stability to find periodic patterns that have a stable periodic behavior. Then, the second limitation is addressed by proposing an algorithm named TSPIN (Top-k Stable Periodic pattern mINer) to find the top-k stable periodic patterns, where the user can directly specify the number of patterns k to be found rather than using the minSup threshold. Several experiments have been performed to assess TSPIN’s performance, and it was found that it is efficient and can discover patterns that reveal interesting insights in real data.

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Data Availability

The code and datasets will be integrated in the SPMF data mining library (http://www.philippe-fournier-viger.com/spmf) following the article acceptance.

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Acknowledgements

This study was partly funded by the National Natural Science Foundation of China and the Harbin Institute of Technology.

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Authors and Affiliations

  1. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Ying Wang & Peng Yang

  3. Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

  4. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Unil Yun

  5. The University of Tokyo, Tokyo, Japan

    Rage Uday Kiran

Authors
  1. Philippe Fournier-Viger
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  2. Ying Wang
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  3. Peng Yang
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  4. Jerry Chun-Wei Lin
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  5. Unil Yun
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Correspondence to Philippe Fournier-Viger.

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This article belongs to the Topical Collection: Special issue on Artificial intelligence in practice - from theory to application

Guest Editors: Franz Wotawa, Gerhard Friedrich and Ingo Pill

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Fournier-Viger, P., Wang, Y., Yang, P. et al. TSPIN: mining top-k stable periodic patterns. Appl Intell 52, 6917–6938 (2022). https://doi.org/10.1007/s10489-020-02181-6

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