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Towards an Optimal Task-Driven Information Granulation

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Information Granularity, Big Data, and Computational Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 8))

Abstract

In this work we have analyzed Big Data sources and made a conclusion that sizeable part of them is people-generated data. We can present this type of data in form of qualitative attributes. The model of such attributes is a collection of fuzzy granules. We also need to granulate the data for application of a big part of analytical technologies. When we form the granules, we have a choice among different variants. Which of them is good for specific task? How can we measure this “goodness” and make a choice the best (optimal) granulation? We provide our vision of answers on these questions in the chapter.

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Author information

Authors and Affiliations

  1. Mathematical Foundations of Intelligent Systems, Department of Mechanics and Mathematics, Lomonosov’ Moscow State University, Moscow, 119899, Russia

    Alexander Ryjov

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  1. Alexander Ryjov
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Correspondence to Alexander Ryjov .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Ryjov, A. (2015). Towards an Optimal Task-Driven Information Granulation. In: Pedrycz, W., Chen, SM. (eds) Information Granularity, Big Data, and Computational Intelligence. Studies in Big Data, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-08254-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-08254-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08253-0

  • Online ISBN: 978-3-319-08254-7

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