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Rough-Granular Computing Based Relational Data Mining

  • Conference paper
Advances on Computational Intelligence (IPMU 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 297))

Abstract

In this paper, we propose a rough-granular computing framework for mining relational data. We adapt the tolerance rough set model for relational data analysis. We introduce two ways for constructing the universe from relational data. Due to applying granular computing methods, one can overcome problems such as relational data representation and the search space limitation. We also show how the proposed framework can be applied to data mining tasks such as classification.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351, Białystok, Poland

    Piotr Hońko

Authors
  1. Piotr Hońko
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Editor information

Editors and Affiliations

  1. Faculty of Economics, University of Catania, Corso Italia, 55, 95129, Catania, Italy

    Salvatore Greco  & Benedetto Matarazzo  & 

  2. Université Pierre et Marie Curie - Paris6, CNRS UMR 7606, DAPA, LIP6 8, rue du Capitaine Scott, F-75015, Paris, France

    Bernadette Bouchon-Meunier

  3. Dip. Matematica e Informatica, Università di Perugia, 06123, Perugia, Italy

    Giulianella Coletti

  4. Department of Computer and Management Science, University of Trento, Via Inama 5, 38122, Trento, Italy

    Mario Fedrizzi

  5. Machine Intelligence Institute, IONA College, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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Hońko, P. (2012). Rough-Granular Computing Based Relational Data Mining. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_30

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  • DOI: https://doi.org/10.1007/978-3-642-31709-5_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31708-8

  • Online ISBN: 978-3-642-31709-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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