Advertisement

Parallel Hybrid SOM Learning on High Dimensional Sparse Data

  • Lukáš Vojáček
  • Jan Martinovič
  • Jiří Dvorský
  • Kateřina Slaninová
  • Ivo Vondrák
Part of the Communications in Computer and Information Science book series (CCIS, volume 245)

Abstract

Self organizing maps (also called Kohonen maps) are known for their capability of projecting high-dimensional space into lower dimensions. There are commonly discussed problems like rapidly increased computational complexity or specific similarity representation in the high-dimensional space. In the paper there is proposed the effective clustering algorithm based on self organizing map with the main purpose to reduce high dimension of the input dataset. The problem of computational complexity is solved using parallelization; the speed of proposed algorithm is accelerated using the algorithm version suitable for data collections with certain level of sparsity.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bekel, H., Heidemann, G., Ritter, H.: Interactive image data labeling using self-organizing maps in an augmented reality scenario. Neural Networks 18(5-6), 566–574 (2005)CrossRefGoogle Scholar
  2. 2.
    Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is ”Nearest Neighbor” Meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Pampalk, E., Rauber, A., Merkl, D.: Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, pp. 871–876. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Gas, B., Chetouani, M., Zarader, J.-L., Charbuillet, C.: Predictive Kohonen Map for Speech Features Extraction. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 793–798. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Georgakis, A., Li, H.: An ensemble of som networks for document organization and retrieval. In: Proceedings of AKRR 2005, International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, pp. 141–147 (2005)Google Scholar
  6. 6.
    Gropp, W., Lusk, E., Skjellum, A.: Using MPI: portable parallel programming with the message-passing inferace. MIT Press (1999)Google Scholar
  7. 7.
    Kishida, K.: Techniques of document clustering: A review. Library and Information Science 35(1), 106–120 (2005)Google Scholar
  8. 8.
    Kohonen, O., Jaaskelainen, T., Hauta-Kasari, M., Parkkinen, J., Miyazawa, K.: Organizing spectral image database using self-organizing maps. Journal of Imaging Science and Technology 49(4), 431–441 (2005)Google Scholar
  9. 9.
    Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer Series in Information Sciences, vol. 8. Springer, Heidelberg (1984) (1989)Google Scholar
  10. 10.
    Kohonen, T.: Things you haven’t heard about the self-organizing map. In: Proc. ICNN 1993, International Conference on Neural Networks, Piscataway, NJ, pp. 1147–1156. IEEE, IEEE Service Center (1993)Google Scholar
  11. 11.
    Kohonen, T.: Exploration of very large databases by self-organizing maps. In: Proceedings of ICNN 1997, International Conference on Neural Networks, PL1–PL6. IEEE Service Center, Piscataway (1997)Google Scholar
  12. 12.
    Kohonen, T.: Self Organizing Maps, 3rd edn. Springer, Heidelberg (2001)CrossRefMATHGoogle Scholar
  13. 13.
    Lawrence, R.D., Almasi, G.S., Rushmeier, H.E.: A scalable parallel algorithm for self-organizing maps with applications to sparse data mining problems. Data Mining and Knowledge Discovery 3, 171–195 (1999)CrossRefGoogle Scholar
  14. 14.
    Machon-Gonzalez, I., Lopez-Garcia, H., Calvo-Rolle, J.L.: A hybrid batch SOM-NG algorithm. In: The 2010 International Joint Conference on Neural Networks, IJCNN (2010)Google Scholar
  15. 15.
    Meenakshisundaram, S., Woo, W.L., Dlay, S.S.: Generalization issues in multiclass classification - new framework using mixture of experts. Wseas Transactions on Information-Science and Applications 4, 1676–1681 (2004)Google Scholar
  16. 16.
    Pullwitt, D.: Integrating contextual information to enhance som-based text document clustering. Neural Networks 15, 1099–1106 (2002)CrossRefGoogle Scholar
  17. 17.
    Vojáček, L., Martinovič, J., Slaninová, K., Dráždilová, P., Dvorský, J.: Combined method for effective clustering based on parallel som and spectral clustering. In: Proceedings of Dateso 2011. CEUR Workshop Proceedings, vol. 706, pp. 120–131 (2011)Google Scholar
  18. 18.
    Wu, C.-H., Hodges, R.E., Wang, C.J.: Parallelizing the self-organizing feature map on multiprocessor systems. Parallel Computing 17(6-7), 821–832 (1991)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lukáš Vojáček
    • 1
  • Jan Martinovič
    • 1
  • Jiří Dvorský
    • 1
  • Kateřina Slaninová
    • 1
    • 2
  • Ivo Vondrák
    • 1
  1. 1.Department of Computer ScienceVŠB – Technical University of OstravaOstravaCzech Republic
  2. 2.Department of InformaticsSBA, Silesian University in OpavaKarvináCzech Republic

Personalised recommendations