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Toward Interactive Computations: A Rough-Granular Approach

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Advances in Machine Learning II

Part of the book series: Studies in Computational Intelligence ((SCI,volume 263))

Abstract

We present an overview of Rough Granular Computing (RGC) approach to modeling complex systems and processes. We discuss the granular methodology in conjunction with paradigms originating in rough sets, such as approximation spaces. We attempt to show the methodology aimed at construction of complex concepts from raw data in hierarchical manner. We illustrate, how the inclusion of domain knowledge, relevant ontologies, and interactive consensus finding leads to more potent granular models for processes.

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Authors and Affiliations

  1. Institute of Mathematics, The University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron & Marcin Szczuka

Authors
  1. Andrzej Skowron
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  2. Marcin Szczuka
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Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Jacek Koronacki  & Sławomir T. Wierzchoń  & 

  2. Woodward Hall 430C, University of North Carolina, 9201 University City Blvd., N.C. 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Systems Research Institute, Polish Academy of Sciences, ul.Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Skowron, A., Szczuka, M. (2010). Toward Interactive Computations: A Rough-Granular Approach. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds) Advances in Machine Learning II. Studies in Computational Intelligence, vol 263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05179-1_2

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  • DOI: https://doi.org/10.1007/978-3-642-05179-1_2

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