Skip to main content
SpringerLink
Log in

Robust Kernel Fuzzy Clustering

  • Conference paper
Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

Included in the following conference series:

Abstract

We present a method for extracting arbitrarily shaped clusters buried in uniform noise data. The popular k-means algorithm is firstly fuzzified with addition of entropic terms to the objective function of data partitioning problem. This fuzzy clustering is then kernelized for adapting to the arbitrary shape of clusters. Finally, the Euclidean distance in this kernelized fuzzy clustering is modified to a robust one for avoiding the influence of noisy background data. This robust kernel fuzzy clustering method is shown to outperform every its predecessor: fuzzified k-means, robust fuzzified k-means and kernel fuzzified k-means algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans. Neural Netw. 13, 780–784 (2002)

    Article  Google Scholar 

  2. Kim, D.-W., Lee, K., Lee, D., Lee, K.H.: Evaluation of the performance of clustering algorithms in kernel-based feature space. Patt. Recog. 38, 607–611 (2004)

    Article  Google Scholar 

  3. Wu, K.-L., Yang, M.-S.: Alternative c-means clustering algorithms. Patt. Recog. 35, 2267–2278 (2002)

    Article  MATH  Google Scholar 

  4. Leski, J.: Towards a robust fuzzy clustering. Fuzzy Sets & Syst. 137, 215–233 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Urahama, K.: Convergence of alternative c-means clustering algorithms. IEICE Trans. Inf. & Syst. E86-D, 752–754 (2003)

    Google Scholar 

  6. Zhang, D.-Q., Chen, S.-C.: Kernel-based fuzzy and possibilistic c-means clustering. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 122–125. Springer, Heidelberg (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kyushu University, Fukuoka-shi, 815-8540, Japan

    Weiwei Du, Kohei Inoue & Kiichi Urahama

Authors
  1. Weiwei Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kohei Inoue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kiichi Urahama
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Du, W., Inoue, K., Urahama, K. (2005). Robust Kernel Fuzzy Clustering. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_58

Download citation

  • DOI: https://doi.org/10.1007/11539506_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28312-6

  • Online ISBN: 978-3-540-31830-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation