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Computational Intelligence pp 431–456Cite as

Fuzzy Data Analysis

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Abstract

So far, we considered fuzzy methods for modeling purposes, for which it is beneficial to incorporate vague concepts. As a consequence, the created (fuzzy) models are designed by domain experts and thus result from a purely knowledge-driven approach. However, fuzzy models may also be derived (automatically) from data if a sufficient amount of suitable data is available (data-driven approach).

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Authors and Affiliations

  1. Otto-von-Guericke-University Magdeburg, Magdeburg, Sachsen-Anhalt, Germany

    Rudolf Kruse, Christian Borgelt, Christian Braune & Sanaz Mostaghim

  2. SAP Innovation Center, Potsdam, Germany

    Matthias Steinbrecher

Authors
  1. Rudolf Kruse
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  2. Christian Borgelt
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  3. Christian Braune
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  4. Sanaz Mostaghim
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  5. Matthias Steinbrecher
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Correspondence to Rudolf Kruse , Christian Borgelt , Christian Braune , Sanaz Mostaghim or Matthias Steinbrecher .

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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Fuzzy Data Analysis. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_20

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  • DOI: https://doi.org/10.1007/978-1-4471-7296-3_20

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