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Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering

  • Dario Malchiodi
  • Witold Pedrycz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8256)

Abstract

We propose an algorithm for inferring membership functions of fuzzy sets by exploiting a procedure originated in the realm of support vector clustering. The available data set consists of points associated with a quantitative evaluation of their membership degree to a fuzzy set. The data are clustered in order to form a core gathering all points definitely belonging to the set. This core is subsequently refined into a membership function. The method is analyzed and applied to several real-world data sets.

Keywords

Membership Function Membership Grade Information Granule Obese Class Machine Learn Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Dario Malchiodi
    • 1
  • Witold Pedrycz
    • 2
  1. 1.Università degli Studi di MilanoItaly
  2. 2.University of AlbertaCanada

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