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Fuzzy Sets and Their Extensions: Representation, Aggregation and Models pp 411–430Cite as

Subgroup Discovery with Linguistic Rules

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

Subgroup discovery can be defined as a form of supervised inductive learning in which, given a population of individuals and a specific property of individuals in which we are interested, find population subgroups that have the most unusual distributional characteristics with respect to the property of interest. Subgroup discovery algorithms aim at discovering individual rules, which must be represented in explicit symbolic form and which must be simple and understandable in order to be recognized as actionable by potential users.

A fuzzy approach for a subgroup discovery process, which considers linguistic variables with linguistic terms in descriptive fuzzy rules, lets us obtain knowledge in a similar way of the human thought process. Linguistic rules are naturally inclined towards coping with linguistic knowledge and to produce more interpretable and actionable solutions. This chapter analyzes the use of linguistic rules for modelling this problem, and shows a genetic extraction model for learning this kind of rules.

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Authors
  1. María José del Jesus
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  2. Pedro González
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  3. Francisco Herrera
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Editor information

Editors and Affiliations

  1. Department of Automatics and Computation, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain

    Humberto Bustince

  2. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

    Francisco Herrera

  3. Faculty of Mathematics, Universidad Complutense, Plaza de las Ciencias 3, 28040 Madrid, Spain

    Javier Montero

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© 2008 Springer-Verlag Berlin Heidelberg

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Jesus, M.J.d., González, P., Herrera, F. (2008). Subgroup Discovery with Linguistic Rules. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_21

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  • DOI: https://doi.org/10.1007/978-3-540-73723-0_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

  • eBook Packages: EngineeringEngineering (R0)

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