Semi-supervised Learning of Dynamic Self-Organising Maps

  • Arthur Hsu
  • Saman K. Halgamuge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


We present a semi-supervised learning method for the Growing Self-Organising Maps (GSOM) that allows fast visualisation of data class structure on the 2D network. Instead of discarding data with missing values, the network can be trained from data with up to 60% of their class labels and 25% of attribute values missing, while able to make class prediction with over 90% accuracy for the benchmark datasets used.


Class Label Supervise Learning Class Structure Benchmark Dataset Data Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amini, M.-R., Gallinari, P.: The use of unlabeled data to improve supervised learning for text summarization. In: Proceedings of the 25th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 105–113 (2002)Google Scholar
  2. 2.
    Jaakkola, T., Szummer, M.: Information regularization with partially labeled data. In: Advances in Neural Information processing systems 15 (2002)Google Scholar
  3. 3.
    Cohen, I., Sebe, N., Cozman, F.G., Huang, T.S.: Semi-supervised learning for facial expression recognition. In: Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval, Berkeley, California, pp. 17–22 (2003)Google Scholar
  4. 4.
    Belkin, M., Niyogi, P.: Using manifold structure for partially labelled classification. In: Proceedings of Advances in Neural Information Processing Systems, vol. 15 (2003)Google Scholar
  5. 5.
    Wickramasinghe, L., Alahakoon, L.: Dynamic self organizing maps for discovery and sharing of knowledge in multi agent systems. Web Intelligence and Agent Systems: An International Journal 3(1) (2005)Google Scholar
  6. 6.
    Alahakoon, D.: Controlling the spread of dynamic self organising maps. Neural Computing and Applications 13(2), 168–174 (2004)Google Scholar
  7. 7.
    Hsu, A., Halgamuge, S.K.: Enhancement of topology preservation and hierarchical dynamic self-organising maps for data visualisation. International Journal of Approximate Reasoning 23(2-3), 259–279 (2003)CrossRefGoogle Scholar
  8. 8.
    Hsu, A., Tang, S., Halgamuge, S.K.: An unsupervised hierarchical dynamic selforganizing approach to cancer class discovery and marker gene identification in microarray data. Bioinformatics 19(16), 2131–2140 (2003)CrossRefGoogle Scholar
  9. 9.
    Fritzke, B.: Growing cell structures - a self-organising network for unsupervised and supervised learning. Neural Networks 7(9), 1441–1460 (1994)CrossRefGoogle Scholar
  10. 10.
    Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic self-organising maps with controlled growth for knowledge discovery. IEEE Transactions on Neural Networks, Special Issue on Knowledge Discovery and Data Mining 11(3) (2000)Google Scholar
  11. 11.
    Kaski, S., Kohonen, T.: Exploratory data analysis by the self-organizing map: Structures of welfare and poverty in the world. In: Proceedings of the Third International Conference on Neural Networks in the Capital Markets, Singapore (1996)Google Scholar
  12. 12.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  13. 13.
    Fahlman, S.E.: CMU benchmark collection of benchmark problems for neural-net learning algorithms (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arthur Hsu
    • 1
  • Saman K. Halgamuge
    • 1
  1. 1.Dynamic Systems and Control Group, Department of Mechanical and Manufacturing EngineeringUniversity of MelbourneAustralia

Personalised recommendations