Skip to main content

Fuzzy-Rough Set Based Nearest Neighbor Clustering Classification Algorithm

  • 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:

  • 1201 Accesses

Abstract

We propose a new nearest neighbor clustering classification algorithm based on fuzzy-rough set theory (FRNNC). First, we make every training sample fuzzy-roughness and use edit nearest neighbor algorithm to remove training sample points in class boundary or overlapping regions, and then use Mountain Clustering method to select representative cluster center points, then Fuzzy-Rough Nearest neighbor algorithm (FRNN) is applied to classify the test data. The new algorithm is applied to hand gesture image recognition, the results show that it is more effective and performs better than other nearest neighbor methods.

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. Keller, J.M., Gray, M.R.: A Fuzzy K-nearest neighbor algorithm. IEEE Transactions on System, Man and Cybernetics 15(4), 580–585 (1985)

    Google Scholar 

  2. Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48–57 (1997)

    Article  MATH  Google Scholar 

  3. Dubois, D., Prade, H.: Rough-fuzzy sets and fuzzy-rough sets. International Journal of General Systems 17(2-3), 191–209 (1990)

    Article  MATH  Google Scholar 

  4. Anna, M.R., Etienne, E.K.: A comparative study of fuzzy rough sets. Fuzzy Sets and Systems 126, 137–155 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  5. Sarkar, M.: Fuzzy-Rough nearest neighbors algorithm. In: Proc. Of IEEE Int. Conference on Systems, Man and Cybernetics, Nashville,Tennessee,USA, pp. 3556–3561 (2000)

    Google Scholar 

  6. Ye, C.Z., Yang, J.: Improving performance of decision trees with muti -edit-nearest-neighbor algorithm. Control and Decision 18(1), 96–102 (2003)

    MathSciNet  Google Scholar 

  7. Yager, R.R., Filev, D.P.: Approximate clustering via the mountain method. IEEE Transactions on Systems, Man and Cybernetics 24(8), 1279–1284 (1994)

    Article  Google Scholar 

  8. Yang, M.S., Wu, K.L.: A Similarity Based Robust Clustering Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(4), 434–448 (2004)

    Article  Google Scholar 

  9. Lingras, P., Yan, R.: Interval clustering using fuzzy and rough set theory. In: Processing of NAFIPS 2004, Alberta, Canada, vol. 2, pp. 780–784 (2004)

    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

Wang, X., Yang, J., Teng, X., Peng, N. (2005). Fuzzy-Rough Set Based Nearest Neighbor Clustering Classification Algorithm. 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_47

Download citation

  • DOI: https://doi.org/10.1007/11539506_47

  • 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