Skip to main content

A Cooperative and Penalized Competitive Learning Approach to Gaussian Mixture Clustering

  • Conference paper
Artificial Neural Networks – ICANN 2010 (ICANN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6354))

Included in the following conference series:

  • 3410 Accesses

Abstract

Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
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.

Similar content being viewed by others

References

  1. MacQueen, J.B.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, Calif., USA, pp. 281–297 (1967)

    Google Scholar 

  2. Xu, L., Krzyzak, A., Oja, E.: Rival Penalized Competitive Learning for Clustering Analysis. RBF Net, and Curve Detection. IEEE Transactions on Neural Networks 4, 636–648 (1993)

    Article  Google Scholar 

  3. Cheung, Y.M.: Rival penalization controlled competitive learning for data clustering with unknown cluster number. In: Proceedings of 9th International Conference on Neural Information Processing, pp. 18–22 (2002)

    Google Scholar 

  4. Cheung, Y.M.: Maximum weighted likelihood via rival penalized EM for density mixture clustering with automatic model selection. IEEE Transactions on Knowledge and Data Engineering 17, 750–761 (2005)

    Article  Google Scholar 

  5. Cheung, Y.M.: A competitive and cooperative learning approach to robust data clustering. In: Proceedings of the IASTED International Conference of Neural Networks and Computational Intelligence (NCI 2004), pp. 131–136 (2004)

    Google Scholar 

  6. Li, T., Pei, W.J., Wang, S.P., Cheung, Y.M.: Cooperation Controlled Competitive Learning Approach for Data Clustering. In: Proceedings of 2008 International Conference on Computational Intelligence and Security (CIS 2008), pp. 24–29 (2008)

    Google Scholar 

  7. Ahalt, S.C., Krishnamurty, A.K., Chen, P., Melton, D.E.: Competitive learning algorithms for vector quantization. Neural Networks 3, 277–291 (1990)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cheung, Ym., Jia, H. (2010). A Cooperative and Penalized Competitive Learning Approach to Gaussian Mixture Clustering. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15825-4_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15824-7

  • Online ISBN: 978-3-642-15825-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics