Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Clustering Analysis of Competitive Learning Network for Molecular Data

  • 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

In this paper competitive learning cluster are used for molecular data of large size sets. The competitive learning network can cluster the input data, it only adapts to the node of winner, the winning node is more likely to win the competition again when a similar input is presented, thus similar inputs are clustered into same a class and dissimilar inputs are clustered into different classes. The experimental results show that the competitive learning network has a good clustering reproducible, indicates the effectiveness of clusters for molecular data, the conscience learning algorithm can effectively cancel the dead nodes when the output nodes increasing, the kinds of network indicates the effectiveness of clusters for molecular data of large size sets.

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

Access this chapter

Log in via an institution

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
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.

Similar content being viewed by others

References

  1. Hsu, D., Figueroa, M., Diorio, C.: Competitive Learning with Floating-Gate Circuits. IEEE Transactions on Neural Networks 13, 732–744 (2002)

    Article  Google Scholar 

  2. Jiang, M., Cai, H., Zhang, B.: Self-Organizing Map Analysis Consistent with Neuroimaging for Chinese Noun, Verb and Class-Ambiguous Word. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 971–976. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Dougherty, E.R., Brun, M.: A probabilistic theory of clustering. Pattern Recognition 37, 917–925 (2004)

    Article  MATH  Google Scholar 

  4. Puntonet, C.G., Mansour, A., Bauer, C., et al.: Separation of Sources Using Simulated Annealing and Competitive Learning. Neurocomputing 49, 39–60 (2002)

    Article  MATH  Google Scholar 

  5. Noe, F.: Transition Networks for the Comprehensive Analysis of Complex Rearrangements in Proteins. Ph.D Dissertation, University of Heidelberg, Germany (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, 100876, China

    Lin Wang & Yinghua Lu

  2. Lab. of Computational Linguistics, School of Humanities and Social Sciences, Tsinghua University, Beijing, 100084, China

    Minghu Jiang

  3. Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Im Neuenheimer Feld 368, 69210, Heidelberg, Germany

    Lin Wang, Minghu Jiang, Frank Noe & Jeremy C. Smith

Authors
  1. Lin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minghu Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yinghua Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frank Noe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeremy C. Smith
    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

Wang, L., Jiang, M., Lu, Y., Noe, F., Smith, J.C. (2006). Clustering Analysis of Competitive Learning Network for Molecular Data. 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_184

Download citation

  • DOI: https://doi.org/10.1007/11759966_184

  • 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

Search

Navigation