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Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

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Abstract

In many fields including medical research, e-business and road transportation, data may vary over time, i.e., new objects and new attributes are added. In this paper, we present a method for dynamically updating approximations based on rough fuzzy sets under the variation of objects and attributes simultaneously in fuzzy decision systems. Firstly, a matrix-based approach is proposed to construct the rough fuzzy approximations on the basis of relation matrix. Then the method for incrementally computing approximations is presented, which involves the partition of the relation matrix and partly changes its element values based the prior matrices’ information. Finally, an illustrative example is employed to validate the effectiveness of the proposed method.

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Acknowledgements

This work is supported by the National Science Foundation of China (No. 61175047), NSAF (No. U1230117) and the Young Software Innovation Foundation of Sichuan Province, China (No. 2014-046).

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Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China

    Yanyong Huang, Tianrui Li & Shi-**n Horng

  2. Tianfu Institute of Mathematics, Tianfu College of Southwestern University of Finance and Economics, Chengdu, 610045, China

    Yanyong Huang

Authors
  1. Yanyong Huang
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  2. Tianrui Li
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  3. Shi-**n Horng
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Corresponding author

Correspondence to Tianrui Li .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tian** University, Tian**, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Huang, Y., Li, T., Horng, Sj. (2015). Dynamic Maintenance of Rough Fuzzy Approximations with the Variation of Objects and Attributes. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

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