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An Extension of Rough Set Approximation to Flow Graph Based Data Analysis

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Rough Sets and Current Trends in Computing (RSCTC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6086))

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Abstract

This paper concerns some aspects of mathematical flow graph based data analysis. In particular, taking a flow graph view on rough sets’ categories and measures leads to a new methodology of inductively reasoning form data. This perspective shows interesting relationships and properties among rough set, flow graphs and inverse flow graphs. A possible car dealer application is outlined and discussed. Evidently, our new categories and measures assist and alleviate some limitations in flow graphs to discover new patterns and explanations.

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Author information

Authors and Affiliations

  1. School of Mathematics, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand, 10520

    Doungrat Chitcharoen & Puntip Pattaraintakorn

Authors
  1. Doungrat Chitcharoen
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  2. Puntip Pattaraintakorn
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Editor information

Editors and Affiliations

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

    Marcin Szczuka

  2. ICS, Warsaw University of Technology,,  

    Marzena Kryszkiewicz

  3. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Manitoba, Canada

    Sheela Ramanna

  4. Dept. of Computer Science, The University of Wales, Aberystwyth, UK

    Richard Jensen

  5. Harbin Institute of Technology, PO Box 458, 150006, Harbin, China

    Qinghua Hu

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Chitcharoen, D., Pattaraintakorn, P. (2010). An Extension of Rough Set Approximation to Flow Graph Based Data Analysis. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_45

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  • DOI: https://doi.org/10.1007/978-3-642-13529-3_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13528-6

  • Online ISBN: 978-3-642-13529-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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