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A Novel Association Rule Mining Method for Streaming Temporal Data

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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.

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Funding

This work is supported in part by the National Key R & D Program of China (No. 2018YFB1003201), the Natural Science Foundation of P. R. China (No. 61672296, No. 61602261, No. 61572260, No. 61872196, No. 61332013, No. 61872194, No. 61902196), Scientific and Technological Support Project of Jiangsu Province (No. BE2017166, and No. BE2019740), Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (No. 18KJA520008), Six Talent Peaks Project of Jiangsu Province (RJFW-111), the ARC Discovery Early Career Research Award (No. DE130100911), the ARC Discovery Project (No. DP130101327), the ARC Linkage Project (No. LP100200682), the International Science and Technology Cooperation Projects (No. 2016D10008, No. 2013DFG12810, No. 2013C24027), the Municipal Natural Science Foundation of Ningbo (No. 2015A610119), the Natural Science Foundation of Zhejiang Province (No. Y16F020002), the Guangzhou Science and Technology Project (No. 2016201604030034), and NUPTSF (No. NY220014 and No. NY220188).

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Authors and Affiliations

  1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

    Hui Zheng & Peng LI

  2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu, China

    Hui Zheng & Peng LI

  3. School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia

    Hui Zheng & Jing HE

Authors
  1. Hui Zheng
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  2. Peng LI
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  3. Jing HE
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Corresponding authors

Correspondence to Peng LI or Jing HE.

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This work does not contain any studies with human participants.

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The datasets used in this work are provided within the main body.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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All the authors contributed equally to this work.

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This work is supported in part by the National Key R & D Program of China (No. 2018YFB1003201), the Natural Science Foundation of P. R. China (No. 61672296, No. 61602261, No. 61572260, No. 61872196, No. 61332013, No. 61872194, No. 61902196), Scientific and Technological Support Project of Jiangsu Province (No. BE2017166, and No. BE2019740), Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (No. 18KJA520008), Six Talent Peaks Project of Jiangsu Province (RJFW-111), the ARC Discovery Early Career Research Award (No. DE130100911), the ARC Discovery Project (No. DP130101327), the ARC Linkage Project (No. LP100200682), the International Science and Technology Cooperation Projects (No. 2016D10008, No. 2013DFG12810, No. 2013C24027), the Municipal Natural Science Foundation of Ningbo (No. 2015A610119), the Natural Science Foundation of Zhejiang Province (No. Y16F020002), the Guangzhou Science and Technology Project (No. 2016201604030034), and NUPTSF (No. NY220014 and No. NY220188).

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Zheng, H., LI, P. & HE, J. A Novel Association Rule Mining Method for Streaming Temporal Data. Ann. Data. Sci. 9, 863–883 (2022). https://doi.org/10.1007/s40745-021-00345-w

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  • DOI: https://doi.org/10.1007/s40745-021-00345-w

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