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)


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.


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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

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