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Knowledge discovery and variable scale evaluation for long series data

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Abstract

Knowledge discovery and evaluation is a challenging but rewarding process of obtaining available information automatically from database. Due to the heterogeneity of the collected data, the connotative knowledge has the characteristics of uncertainty, random occurrence and variable scale. Therefore, an unsupervised knowledge discovery and variable scale evaluation model is presented in this paper based on a new multi-feature fusion method. Firstly, point at the multiple information features, an amplitude-frequency-shape based state description form is proposed in this paper. It could analyze the time series from the aspects of energy, phase, and knowledge similarity. In view of the variable number and scale of knowledge fragments, a piecewise linear segmentation criterion is put forward based on the complexity and accuracy of information representation. Then a model free knowledge discovery framework without samples labels is constructed to discover the knowledge quickly and effectively. Aimed at the variable knowledge scale, a variable scale evaluation method is first proposed to distinguish the multi-scale decision-making knowledge based on the indicators of system stability and security. It could optimize the knowledge base and guide the decision-making process. The experimental results on heterogeneous activity datasets indicate that the proposed method here could generally analysis the time series state and discover the knowledge efficiently from massive data. In addition, the knowledge discovery and evaluation at a continuous decision system show that the proposed framework could meet the needs of knowledge discovery in complex environment and effectively distinguish the knowledge to provide strong support for establishing a credible decision-making system.

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Data availability

The UCI datasets analyzed during the current study are available from UCI Machine Learning Repository (http://archive.ics.uci.edu/ml). The blast furnace gas scheduling activities dataset that support the findings of this study is not openly available due to the sharing agreement with the enterprise.

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Acknowledgements

The authors wish to thank the Associate Editor and the anonymous reviewers for their valuable comments and constructive suggestions, which helped improve the presentation of the paper. This work was supported by the National Key R&D Program of China under Grant 2017YFA0700300, the National Natural Sciences Foundation of China under Grant 61833003, Grant 61873048, Grant U1908218, the Fundamental Research Funds for the Central Universities under Grant DUT22JC16.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian, China

    Yanwei Zhai, Zheng Lv, Jun Zhao & Wei Wang

  2. School of Control Science and Engineering, Dalian University of Technology, Dalian, China

    Yanwei Zhai, Zheng Lv, Jun Zhao & Wei Wang

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  1. Yanwei Zhai
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  2. Zheng Lv
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Correspondence to Zheng Lv.

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Zhai, Y., Lv, Z., Zhao, J. et al. Knowledge discovery and variable scale evaluation for long series data. Artif Intell Rev 56, 3157–3180 (2023). https://doi.org/10.1007/s10462-022-10250-0

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