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Linguistic Fuzzy Rules in Data Mining: Follow-Up Mamdani Fuzzy Modeling Principle

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Combining Experimentation and Theory

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 271))

Abstract

From the definition of fuzzy sets by Zadeh in 1965, fuzzy logic has become a significant area of interest for researchers on artificial intelligence. In particular, Professor Mamdani was the pioneer who investigated the use of fuzzy logic for interpreting the human derived control rules, and therefore his work was considered a milestone application of this theory.

In this work, we aim to carry out an overview of the principles of fuzzy modeling given by Mamdani and its application to different areas of data mining that can be exploited such as classification, association rule mining or subgroup discovery, among others. Specifically, we present a case of study on classification with highly imbalanced data-sets in which linguistic fuzzy rule based systems have shown to achieve a good behaviour among other techniques such as decision trees.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Jaén, 23071, Jaén, Spain

    A. Fernández

  2. Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, 18071, Granada, Spain

    F. Herrera

Authors
  1. A. Fernández
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  2. F. Herrera
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Correspondence to A. Fernández .

Editor information

Editors and Affiliations

  1. European Center for Soft Computing, Mieres, Asturias, 33600, Spain

    Enric Trillas

  2. , Computing and Decision Sciences, Coolidge Fellow GE Global Research, NY, 12309, USA

    Piero P. Bonissone

  3. , Edificio Científico-Tecnológico, European Centre of Soft Computing, Calle Gonzalo Gutiérrez Quirós S/N Mieres, Asturias, 33600, Spain

    Luis Magdalena

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

    Janusz Kacprzyk

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Fernández, A., Herrera, F. (2012). Linguistic Fuzzy Rules in Data Mining: Follow-Up Mamdani Fuzzy Modeling Principle. In: Trillas, E., Bonissone, P., Magdalena, L., Kacprzyk, J. (eds) Combining Experimentation and Theory. Studies in Fuzziness and Soft Computing, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24666-1_8

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  • DOI: https://doi.org/10.1007/978-3-642-24666-1_8

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