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A Particle Swarm Clustering Algorithm with Fuzzy Weighted Step Sizes

  • Alexandre SzaboEmail author
  • Myriam Regattieri Delgado
  • Leandro Nunes de Castro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

This paper proposes a modification in the Fuzzy Particle Swarm Clustering (FPSC) algorithm such that membership degrees are used to weight the step size in the direction of the local and global best particles, and in its movement in the direction of the input data at every iteration. This results in the so-called Membership Weighted Fuzzy Particle Swarm Clustering (MWFPSC). The modified algorithm was applied to six benchmark datasets from the literature and its results compared to that of the standard FPSC and FCM algorithms. By introducing these modifications it could be observed a gain in accuracy, representativeness of the clusters found and the final Xie-Beni index, at the expense of a slight increase in the practical computational time of the algorithm.

Keywords

Fuzzy clustering Particle swarm Data mining FPSC 

Notes

Acknowledgment

The authors thank FAPESP, CNPq, and CAPES for their financial support.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alexandre Szabo
    • 1
    • 3
    Email author
  • Myriam Regattieri Delgado
    • 2
  • Leandro Nunes de Castro
    • 3
  1. 1.Federal University of ABCSanto AndréBrazil
  2. 2.Federal University of Technology of ParanáCuritibaBrazil
  3. 3.Natural Computing LaboratoryMackenzie UniversitySão PauloBrazil

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