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Fuzzy Classification of Nonconvex Data-Inherent Structures

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Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods (IPMU 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 80))

Abstract

The present work dedicates itself to the aggregation of nonconvex data-inherent structures into fuzzy classes. A key feature of this aggregation is its conduction within a closed fuzzy classification framework, being built around a single, generic type of a convex membership function. After a short elaboration concerning this essential building block a novel automated, data-driven design strategy to aggregate complex (nonconvex) data-inherent structures is introduced. The whole aggregation process will be illustrated with the help of an example.

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Author information

Authors and Affiliations

  1. Chemnitz University of Technology, D-09107, Chemnitz, Germany

    Arne-Jens Hempel & Steffen F. Bocklisch

Authors
  1. Arne-Jens Hempel
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  2. Steffen F. Bocklisch
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Editor information

Editors and Affiliations

  1. Fachbereich Mathematik und Informatik, Philipps-Universität Marburg, Marburg, Germany

    Eyke Hüllermeier

  2. Department of Knowledge Processing and Language Engineering, Otto-von-Guericke University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Rudolf Kruse

  3. Fakultät für Elektrotechnik und Informationstechnik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Frank Hoffmann

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Hempel, AJ., Bocklisch, S.F. (2010). Fuzzy Classification of Nonconvex Data-Inherent Structures. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods. IPMU 2010. Communications in Computer and Information Science, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14055-6_43

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  • DOI: https://doi.org/10.1007/978-3-642-14055-6_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14054-9

  • Online ISBN: 978-3-642-14055-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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