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Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data

Author

Listed:
  • Aihua Zhu

    (School of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, China
    These authors contributed equally to this work.)

  • Haote Zhang

    (School of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, China
    These authors contributed equally to this work.)

  • Xingqian Chen

    (School of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, China
    These authors contributed equally to this work.)

  • Dingkun Zhu

    (School of Computer Engineering, Jiangsu University of Technology, No. 1801, Zhongwu Avenue, Zhonglou District, Changzhou 213024, China)

Abstract

This paper introduces a dual-strategy model based on temporal transformation and fuzzy theory, and designs a partitioned mining algorithm for periodic frequent patterns in large-scale event data (3P-TFT). The model reconstructs original event data through temporal reorganization and attribute fuzzification, preserving data continuity distribution characteristics while enabling efficient processing of multidimensional attributes within a multi-temporal granularity calendar framework. The 3P-TFT algorithm employs temporal interval and object attribute partitioning strategies to achieve distributed mining of large-scale data. Experimental results demonstrate that this method effectively reveals hidden periodic patterns in stock trading events at specific temporal granularities, with volume–price association rules providing significant predictive and decision-making value. Furthermore, comparative algorithm experiments confirm that the 3P-TFT algorithm exhibits exceptional stability and adaptability across event databases with various cycle lengths, offering a novel theoretical tool for complex event data mining.

Suggested Citation

  • Aihua Zhu & Haote Zhang & Xingqian Chen & Dingkun Zhu, 2025. "Multiscale Fuzzy Temporal Pattern Mining: A Block-Decomposition Algorithm for Partial Periodic Associations in Event Data," Mathematics, MDPI, vol. 13(8), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1349-:d:1638725
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