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Generalized Fuzzy c-Means Clustering and Its Theoretical Properties

  • Yuchi Kanzawa
  • Sadaaki Miyamoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)

Abstract

This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers standard fuzzy c-means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits similar behavior to that of standard fuzzy c-means clustering.

Keywords

Fuzzy c-means clustering Fuzzy classification function 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Shibaura Institute of TechnologyTokyoJapan
  2. 2.University of TsukubaTsukubaJapan

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