Skip to main content

KFCSA: A Novel Clustering Algorithm for High-Dimension Data

  • 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

Classical fuzzy c-means and its variants cannot get better effect when the characteristic of samples is not obvious, and these algorithms run easily into locally optimal solution. According to the drawbacks, a novel mercer kernel based fuzzy clustering self-adaptive algorithm(KFCSA) is presented. Mercer kernel method is used to map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. A self-adaptive algorithm is proposed to decide the number of clusters, which is not given in advance, and it can be gotten automatically by a validity measure function. In addition, attribute reduction algorithm is used to decrease the numbers of attributes before high dimensional data are clustered. Finally, experiments indicate that KFCSA may get better performance.

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

Access this chapter

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. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symposium, pp. 281–297 (1967)

    Google Scholar 

  2. Hartigan, J., Wang, M.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)

    Article  MATH  Google Scholar 

  3. Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithm. Plenum Press, New York (1981)

    Google Scholar 

  4. Jain, A., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)

    MATH  Google Scholar 

  5. Wallace, R.: Finding natural clusters through entropy minimization. Ph.D Thesis. CarnegieMellon University, CS Dept. (1989)

    Google Scholar 

  6. Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)

    Article  Google Scholar 

  7. Girolami, M.: Mercer kernel based clustering in feature space. IEEE Trans Neural Network 13(3), 780–784 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, K., Liu, Y. (2005). KFCSA: A Novel Clustering Algorithm for High-Dimension Data. 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_67

Download citation

  • DOI: https://doi.org/10.1007/11539506_67

  • 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