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A Novel Association Rule Mining Method for Streaming Temporal Data

Author

Listed:
  • Hui Zheng

    (Nanjing University of Posts and Telecommunications
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks
    Swinburne University of Technology)

  • Peng LI

    (Nanjing University of Posts and Telecommunications
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks)

  • Jing HE

    (Swinburne University of Technology)

Abstract

Streaming temporal data contains time stamps and values, challenging to quantify relationships of time stamps and corresponding values. Moreover, the characteristics and relationships of streaming temporal data are not invariable. Thus, it is impossible to analyse all data by a trained model at the beginning of data streams. Practically, the trained model to analyse streaming temporal data should change according to the increasing volume of data. Association rule mining, on the other hand, can find potential relationships from given data. This paper proposes an association rule mining method for streaming temporal data to discover potential relationships from streaming temporal data. Our experiments verify our proposed method. A public data set is applied to compare the performance of the proposed method and its counterpart. A small data set is also applied for two case studies to further illustrate our proposed method mine association rules with streaming temporal data with time stamps and corresponding values.

Suggested Citation

  • Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:4:d:10.1007_s40745-021-00345-w
    DOI: 10.1007/s40745-021-00345-w
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