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An Improved Parallel Method for Computing Rough Set Approximations

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Foundations of Intelligent Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 277))

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Abstract

Parallel computing refers to the practice of exploiting parallelism in computing to achieve higher performance. Rough set theory plays a fundamental role in data analysis, which was extensively used in the context of data mining. The lower and upper approximations are the basic tools in rough set theory. The fast calculation of approximations can effectively improve the efficiency of rough set theory-based approaches. In this paper, we propose a new parallel strategy for computing approximations, which is able to exploit parallelism at all levels of the computation. An illustrative example is given to demonstrate the effectiveness and validity of the proposed method.

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Acknowledgments

This work is supported by the National Science Foundation of China (nos. 61175047, 71201133, 61100117, and 61262058) and NSAF (no. U1230117), the Youth Social Science Foundation of the Chinese Education Commission (nos. 10YJCZH117 and 11YJC630127), and the Fundamental Research Funds for the Central Universities (nos. SWJTU11ZT08, SWJTU12CX091, and SWJTU12CX117).

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Authors and Affiliations

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

    Chuan Luo, Tianrui Li, Junbo Zhang, Anping Zeng & Hongmei Chen

Authors
  1. Chuan Luo
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  2. Tianrui Li
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  3. Junbo Zhang
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  4. Anping Zeng
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  5. Hongmei Chen
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Corresponding author

Correspondence to Tianrui Li .

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Editors and Affiliations

  1. College of Computer and Software Engineering, Shenzhen University, Shenzhen, China

    Zhenkun Wen

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

    Tianrui Li

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Luo, C., Li, T., Zhang, J., Zeng, A., Chen, H. (2014). An Improved Parallel Method for Computing Rough Set Approximations. In: Wen, Z., Li, T. (eds) Foundations of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 277. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54924-3_3

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  • DOI: https://doi.org/10.1007/978-3-642-54924-3_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54923-6

  • Online ISBN: 978-3-642-54924-3

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