Skip to main content
Springer Nature Link
Log in

Fuzzy Support Vector Clustering

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

Included in the following conference series:

Abstract

Support vector clustering (SVC) faces the same over-fitting problem as support vector machine (SVM) caused by outliers or noises. Fuzzy support vector clustering (FSVC) algorithm is presented to deal with the problem. The membership model based on k-NN is used to determine the membership value of training samples. The proposed fuzzy support vector clustering algorithm is used to determine the clusters of some benchmark data sets. Experimental results indicate that the proposed algorithm actually reduces the effect of outliers and yields better clustering quality than SVC and traditional centroid-based hierarchical clustering algorithm do.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  2. Ben-Hur, A., Horn, D., Sidgelmann, H.T.: Support Vector Clustering. Journal of Machine Learning Research 2(2), 125–137 (2001)

    Article  Google Scholar 

  3. Huang, H.-P., Liu, Y.-H.: Fuzzy Support Vector Machines for Pattern Recognition and Data Mining. International Journal of Fuzzy Systems 4(3), 826–835 (2002)

    MathSciNet  Google Scholar 

  4. Lin, C.-F., Wang, S.-D.: Fuzzy Support Vector Machines. IEEE Trans. on Neural Networks 13(2), 464–471 (2002)

    Article  Google Scholar 

  5. Merz, C.J., Murphy, P.M.: UCI Repository for Machine Learning Data-Bases. Department of Information and Computer Science. University of California, Irvine (1998), http://www.ics.uci.edu/,~mlearn/MLRepository.html

Download references

Author information

Authors and Affiliations

  1. College of Information and Electronic Engineering, Zhejiang Gongshang University, Hang Zhou, 310035, P.R. China

    En-Hui Zheng

  2. National Lab. of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hang Zhou, 310027, P.R. China

    En-Hui Zheng, Ping Li & Zhi-Huan Song

  3. Computer School, Hangzhou Dianzi University, Hang Zhou, 310018, P.R. China

    Min Yang

Authors
  1. En-Hui Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhi-Huan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zheng, EH., Yang, M., Li, P., Song, ZH. (2006). Fuzzy Support Vector Clustering. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_154

Download citation

  • DOI: https://doi.org/10.1007/11759966_154

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics