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Knowledge Approximations in Multi-scale Ordered Information Systems

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Rough Sets and Knowledge Technology (RSKT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8818))

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Abstract

The key to granular computing is to make use of granules in problem solving. However, there are different granules at different levels of scale in data sets having hierarchical scale structures. And in real-world applications, there may exist multiple types of data in ordered information systems. Therefore, the concept of multi-scale ordered information systems is first introduced in this paper. The lower and upper approximations in multi-scale ordered information systems are then defined, and their properties are examined.

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Author information

Authors and Affiliations

  1. School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang, 316022, P.R. China

    Shen-Ming Gu, Yi Wu, Wei-Zhi Wu & Tong-Jun Li

Authors
  1. Shen-Ming Gu
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  2. Yi Wu
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  3. Wei-Zhi Wu
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  4. Tong-Jun Li
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Corresponding author

Correspondence to Shen-Ming Gu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    Duoqian Miao

  2. Department of Electrical and Computer En, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  3. University of Warsaw, Warsaw, Poland

    Dominik Ślȩzak

  4. University of Applied Sciences, München, Germany

    Georg Peters

  5. Tianjin University, Tianjin, China

    Qinghua Hu

  6. Tongji University, Shanghai, China

    Ruizhi Wang

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© 2014 Springer International Publishing Switzerland

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Gu, SM., Wu, Y., Wu, WZ., Li, TJ. (2014). Knowledge Approximations in Multi-scale Ordered Information Systems. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_48

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  • DOI: https://doi.org/10.1007/978-3-319-11740-9_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11739-3

  • Online ISBN: 978-3-319-11740-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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