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Multi-granularity Intelligent Information Processing

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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))

Abstract

Multi-granularity thinking, computation and problem solving are effective approaches for human being to deal with complex and difficult problems. Deep learning, as a successful example model of multi-granularity computation, has made significant progress in the fields of face recognition, image automatic labeling, speech recognition, and so on. Its idea can be generalized as a model of solving problems by joint computing on multi-granular information/knowledge representation (MGrIKR) in the perspective of granular computing (GrC). This paper introduces our research on constructing MGrIKR from original datasets and its application in big data processing. Firstly, we have a survey about the study of the multi-granular computing (MGrC), including the four major theoretical models (rough sets, fuzzy sets, quotient space,and cloud model) for MGrC. Then we introduce the five representative methods for constructing MGrIKR based on rough sets, computing with words(CW), fuzzy quotient space based on information entropy, adaptive Gaussian cloud transformation (A-GCT), and multi-granularity clustering based on density peaks, respectively. At last we present an MGrC based big data processing framework, in which MGrIKR is built and taken as the input of other machine learning and data mining algorithms.

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Acknowledgement

This work is partly supported by the National Natural Science Foundation of China under Grant numbers of 61272060, 61472056 and 61305055, and Natural Science Foundation Key Project of Chongqing of P. R. China under Grant No. CSTC2013jjB40003.

Author information

Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Guoyin Wang & Qinghua Zhang

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

    Ji Xu

  3. Institute of Electronic Information Technology, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing, 401122, China

    Ji Xu

  4. Chinese Institute of Command and Control, Beijing, 100089, China

    Yuchao Liu

Authors
  1. Guoyin Wang
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  2. Ji Xu
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  3. Qinghua Zhang
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  4. Yuchao Liu
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Corresponding author

Correspondence to Guoyin Wang .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, 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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Wang, G., Xu, J., Zhang, Q., Liu, Y. (2015). Multi-granularity Intelligent Information Processing. 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_4

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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