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Study on Reduction of Multi-Dimensional Qualitative Variables with Granular Computing

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Proceedings of the 2012 International Conference of Modern Computer Science and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 191))

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Abstract

Based on the analysis of large complicated, multi-dimensional qualitative (MDQ) data is described by using the information system in rough set theory. Because there exists some deficiencies in reduction method of identification matrix, a model of MDQ variable reduction is given based on granular computing, and an example verifies its feasibility and advantages, which provides a new idea for the analysis of large complicated qualitative data.

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References

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Author information

Authors and Affiliations

  1. School of Science, University of Science and Technology, Liaoning, China

    Liu Hong, Yang Shen & Li Haiyan

Authors
  1. Liu Hong
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  2. Yang Shen
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  3. Li Haiyan
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Editor information

Editors and Affiliations

  1. Engineering Research Center, Information Technology & Industrial, No 10 Ruanjianyuanzhong Road, Wuhan, China, People's Republic

    Zhenyu Du

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© 2013 Springer-Verlag Berlin Heidelberg

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Hong, L., Shen, Y., Haiyan, L. (2013). Study on Reduction of Multi-Dimensional Qualitative Variables with Granular Computing. In: Du, Z. (eds) Proceedings of the 2012 International Conference of Modern Computer Science and Applications. Advances in Intelligent Systems and Computing, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33030-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-33030-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33029-2

  • Online ISBN: 978-3-642-33030-8

  • eBook Packages: EngineeringEngineering (R0)

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