Skip to main content
SpringerLink
Log in

Differential Evolution Fuzzy Clustering Algorithm Based on Kernel Methods

  • Conference paper
Rough Sets and Knowledge Technology (RSKT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4062))

Included in the following conference series:

Abstract

A new fuzzy clustering algorithm is proposed. By using kernel methods, this paper maps the data in the original space into a high-dimensional feature space in which a fuzzy dissimilarity matrix is constructed. It not only accurately reflects the difference of attributes among classes, but also maps the difference among samples in the high-dimensional feature space into the two-dimensional plane. Using the particularity of strong global search ability and quickly converging speed of Differential Evolution (DE) algorithms, it optimizes the coordinates of the samples distributed randomly on a plane. The clustering for random distributing shapes of samples is realized. It not only overcomes the dependence of clustering validity on the space distribution of samples, but also improves the flexibility of the clustering and the visualization of high-dimensional samples. Numerical experiments show the effectiveness of the proposed algorithm.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Yang, M.S., Hwang, P.Y., Chen, D.H.: Fuzzy clustering algorithms for mixed feature variables. Fuzzy Sets and Systems 141, 301–317 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  2. Kim, D.W., Lee, K.H., Lee, D.: A novel initialization scheme for the fuzzy c-means algorithm for color clustering. Pattern Recognition Letters 25, 227–237 (2004)

    Article  Google Scholar 

  3. Cinquea, L., Foresti, G., Lombardi, L.: A clustering fuzzy approach for image segmentation. Pattern Recognition 37, 1797–1807 (2004)

    Article  Google Scholar 

  4. Xu, R., Donld Wunsch, H.: Survey of clustering algorithms. IEEE transactions on neural networks 16, 645–678 (2005)

    Article  Google Scholar 

  5. Colin, C.: Kernel Methods: A Survey of Current Techniques. Neurocomputing 48, 63–84 (2002)

    Article  MATH  Google Scholar 

  6. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimiza-tion over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  7. Zheng, Y., Zhou, C.G., Wang, S.S., et al.: A dynamic clustering based on genetic algorithm. In: Proceedings of 2003 International Conference on Machine Learning and Cybernetics, November 2-5, 2003, pp. 222–224. Xi’an, China Institute of Electrical and Electronics Engineers Inc. (2003)

    Google Scholar 

  8. Sandra, P., Thiemo, K.: Differential evolution and particle swarm optimisation in partitional clustering. Computational Statistics & Data Analysis 50, 1220–1247 (2006)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Changchun, 130012, P.R. China

    Libiao Zhang, Ming Ma, Xiaohua Liu, Caitang Sun, Miao Liu & Chunguang Zhou

Authors
  1. Libiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaohua Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caitang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chunguang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. College of Computer Science and Technology, Chongqing, University of Posts and Telecommunication, 400065, Chongqing, P.R. China

    Guo-Ying Wang

  2. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Department of Computer Science, University of Regina, Regina,, S4S 0A2, Saskatchewan, Canada

    Yiyu Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhang, L., Ma, M., Liu, X., Sun, C., Liu, M., Zhou, C. (2006). Differential Evolution Fuzzy Clustering Algorithm Based on Kernel Methods. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_62

Download citation

  • DOI: https://doi.org/10.1007/11795131_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-36297-5

  • Online ISBN: 978-3-540-36299-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation