Skip to main content
SpringerLink
Log in

The Evolving Tree—A Novel Self-Organizing Network for Data Analysis

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

The Self-Organizing Map (SOM) is one of the best known and most popular neural network-based data analysis tools. Many variants of the SOM have been proposed, like the Neural Gas by Martinetz and Schulten, the Growing Cell Structures by Fritzke, and the Tree-Structured SOM by Koikkalainen and Oja. The purpose of such variants is either to make a more flexible topology, suitable for complex data analysis problems or to reduce the computational requirements of the SOM, especially the time-consuming search for the best-matching unit in large maps. We propose here a new variant called the Evolving Tree which tries to combine both of these advantages. The nodes are arranged in a tree topology that is allowed to grow when any given branch receives a lot of hits from the training vectors. The search for the best matching unit and its neighbors is conducted along the tree and is therefore very efficient. A comparison experiment with high dimensional real world data shows that the performance of the proposed method is better than some classical variants of SOM.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kohonen, T.: Self-Organizing Maps. Berlin: Springer-Verlag, (1995)).

    Google Scholar 

  2. Pakkanen, J.: The Evolving Tree, a new kind of self-organizing neural network. In: Proceedings of the Workshop on Self-Organizing Maps '03, Kitakyushu, Japan, pp. 311-316, 2003.

  3. Blackmore, J. and Miikkulainen, R.: Incremental grid growing:encoding high-dimensional structure into a two-dimensional feature map. In:Proceedings of the IEEE International Conference on Neural Networks, vol. 1. pp. 450-455, 1993.

  4. Fritzke, B.: Growing cell structures-a self-organizing network for unsupervised and supervised learning. Neural Networks 7 (9)(1994), 1441–1460.

    Google Scholar 

  5. Martinez, T. and Schulten, K.: A “Neural-Gas ”Network Learns Topologies. In: T. Kohonen, K. M ¨akisara, O. Simula and J. Kangas (eds.), Arti cial Neural Networks, vol. 1. Amsterdam, pp. 297-402, 1991.

  6. Bruske, J. and Sommer, G.: Dynamic cell structure learns perfectly topology preserving map. Neural Computation 7 (4)(1997), 845–865.

    Google Scholar 

  7. Koikkalainen, P. and Oja, E.: Self-Organizing hierarchical feature maps. In:Proceedings of 1990 International Joint Conference on Neural Networks, vol. II. San Diego, CA, pp. 279-284, 1990.

  8. Laaksonen, J., Koskela, M., and Oja, E.: PicSOM— self-organizing image retrieval with MPEG-7 content descriptors. IEEE Transactions on Neural Networks 13 (4)(2002), 841–853.

    Google Scholar 

  9. Dittenbach, M., Rauber, A., and Merkl, D.: Recent advances with the growing hierar-chical self-organizing map. In:Proceedings of the 3rd Workshop on Self-Organizing Maps, Lincoln, England, pp. 140-145, 2001. http://www.ifs.tuwien.ac.at.

    Google Scholar 

  10. Desieno, D.: Adding a conscience to competitive learning. In:Proceedings of the International Conference on Neural Networks, vol. I, New York, pp. 117-124, 1998.

  11. Chávez, E., Navarro, G., Baeza-Yates, R., and Marroquin J. L.: Searching in metric spaces. ACM Computing Surveys 33 (1)(2001), 273–321.

    Google Scholar 

  12. Laaksonen, J., Koskela, M., Laakso, S., and Oja, E.: PicSOM—Content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21 (13-14)(2000), 1199–1207.

    Google Scholar 

  13. Manjunath, B. S., Ohm, J.-R., Vasudevan, V. V., and Yamada, A.: Color and texture de-scriptors. IEEE Transactions on Circuits and Systems for Video Technology 11 (6)(2001)).

  14. MPEG-7:MPEG-7 Multimedia Content Description Interface — Part 3 Visual. ISO/IECJTC1/SC29/WG11 W3703 2001.

  15. MPEG-7: MPEG-7 Visual Part of the eXperimentation Model (version 9.0). ISO/IEC JTC1/SC29/WG11 N3914, 2001.

  16. Pakkanen, J., Ilvesm ¨aki, A. and Iivarinen, J.: 'Defect image classi cation and retrieval with MPEG-7 descriptors '. In:J. Bigun and T. Gustavsson (eds.), Proceedings of the 13th Scandinavian Conference on Image Analysis, G¨oteborg, Sweden, pp. 349–355, 2003.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. Box, 5400, FIN-02015, HUT, Finland

    Jussi Pakkanen, Jukka Iivarinen & Erkki Oja

Authors
  1. Jussi Pakkanen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jukka Iivarinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erkki Oja
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pakkanen, J., Iivarinen, J. & Oja, E. The Evolving Tree—A Novel Self-Organizing Network for Data Analysis. Neural Processing Letters 20, 199–211 (2004). https://doi.org/10.1007/s11063-004-2156-8

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-004-2156-8

Search

Navigation