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Incremental fuzzy temporal association rule mining using fuzzy grid table

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Abstract

Traditional temporal association rules mining algorithms cannot dynamically update the temporal association rules within the valid time interval with increasing data. In this paper, a new algorithm called incremental fuzzy temporal association rule mining using fuzzy grid table (IFTARMFGT) is proposed by combining the advantages of boolean matrix with incremental mining. First, multivariate time series data are transformed into discrete fuzzy values that contain the time intervals and fuzzy membership. Second, in order to improve the mining efficiency, the concept of boolean matrices was introduced into the fuzzy membership to generate a fuzzy grid table to mine the frequent itemsets. Finally, in view of the Fast UPdate (FUP) algorithm, fuzzy temporal association rules are incrementally mined and updated without repeatedly scanning the original database by considering the lifespan of each item and inheriting the information from previous mining results. The experiments show that our algorithm provides better efficiency and interpretability in mining temporal association rules than other algorithms.

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Acknowledgements

This research work was supported by the National Natural Science Foundation of China(Grant No.62076025 and No.61572073),the Fundamental Research Funds for the Central Universities(FRF-GF-19-014B).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Ling Wang, Lingpeng Gui & Hui Zhu

  2. Key Laboratory of Knowledege for Industrial Process (Ministry of Education), Beijing, 100083, China

    Ling Wang, Lingpeng Gui & Hui Zhu

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  1. Ling Wang
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  2. Lingpeng Gui
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  3. Hui Zhu
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Correspondence to Ling Wang.

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Wang, L., Gui, L. & Zhu, H. Incremental fuzzy temporal association rule mining using fuzzy grid table. Appl Intell 52, 1389–1405 (2022). https://doi.org/10.1007/s10489-021-02407-1

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