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Australasian Joint Conference on Artificial Intelligence

AI 2005: AI 2005: Advances in Artificial Intelligence pp 1138–1141Cite as

An Adaptive Fuzzy c-Means Algorithm with the L 2 Norm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3809))

Abstract

An extension of the fuzzy c-means clustering algorithm based on an adaptive distance is presented. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive L 2 distance that changes at each algorithm iteration. Experiments with real and synthetic data sets show the usefulness of this method.

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References

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Author information

Authors and Affiliations

  1. Centro de Informática - CIn / UFPE, Av. Prof. Luiz Freire, s/n, Cidade Universitária, CEP: 50740-540, Recife, PE, Brasil

    Nicomedes L. Cavalcanti Júnior & Francisco de A. T. de Carvalho

Authors
  1. Nicomedes L. Cavalcanti Júnior
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  2. Francisco de A. T. de Carvalho
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Editor information

Editors and Affiliations

  1. Guangxi Normal University, College of CS and IT, Guilin, China, and University of Technology, Faculty of Engineering and Information Technology, Sydney, Australia

    Shichao Zhang

  2. Department of Electrical and Computer Systems Engineering, Monash University, 3800, Melbourne, Victoria, Australia

    Ray Jarvis

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Cavalcanti Júnior, N.L., de Carvalho, F.d.A.T. (2005). An Adaptive Fuzzy c-Means Algorithm with the L 2 Norm. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_156

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  • DOI: https://doi.org/10.1007/11589990_156

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30462-3

  • Online ISBN: 978-3-540-31652-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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