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Rough Control Rule Mining Model Based on Decision Interval Concept Lattice and Its Application

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Big Data Technology and Applications (BDTA 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 590))

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Abstract

Fusing the structure feature of interval concept lattice and the actual needs of rough control rules, we have constructed the decision interval concept lattice, further more, we also have built a rules mining model of rough control based on decision interval concept lattice, in order to achieve the optimality between rough control mining cost and control efficiency. Firstly, we have preprocessed the collected original data, so that we can transform it into Boolean formal context form, and then we have constructed the decision interval concept lattice in rough control; secondly, we have established the control rules mining algorithm based on decision interval concept lattice. By analyzing and judging redundant rules, we have formed the rough control association rule base in end. Analysis shows that under the premise of improving the reliability of rules, we have achieved the rough control optimization goal between cost and efficiency. Finally, the model of reservoir scheduling has verified its feasibility and efficiency.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Grant No. 61370168, 61472340), Conditional Construction Project of Hebei Province Technology Hall (Grant No. 14960112D). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. College of Science, North China University of Science and Technology, Tangshan, Hebei, China

    Ailing Sun, Chunying Zhang, Liya Wang & Zhijiang Wang

Authors
  1. Ailing Sun
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  2. Chunying Zhang
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  3. Liya Wang
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  4. Zhijiang Wang
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Corresponding authors

Correspondence to Chunying Zhang or Zhijiang Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Dept. of Computer Science and Technology, Beijing, China

    Wenguang Chen

  2. Harbin Engineering University, China

    Guisheng Yin

  3. South China Normal University, Guangzhou, China

    Gansen Zhao

  4. Harbin Engineering University, China

    Qilong Han

  5. Northeast Forestry University, Harbin, China

    Weipeng Jing

  6. Harbin Univ. of Science and Technology, Harbin, China

    Guanglu Sun

  7. Harbin Sea of Clouds & Computer Tech., Harbin, China

    Zeguang Lu

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© 2016 Springer Science+Business Media Singapore

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Sun, A., Zhang, C., Wang, L., Wang, Z. (2016). Rough Control Rule Mining Model Based on Decision Interval Concept Lattice and Its Application. In: Chen, W., et al. Big Data Technology and Applications. BDTA 2015. Communications in Computer and Information Science, vol 590. Springer, Singapore. https://doi.org/10.1007/978-981-10-0457-5_9

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  • DOI: https://doi.org/10.1007/978-981-10-0457-5_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0456-8

  • Online ISBN: 978-981-10-0457-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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