An Application of the Self-Organizing Map to Multiple View Unsupervised Learning

  • Tomasz Gałkowski
  • Artur Starczewski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7268)


In various data mining applications performing the task of extracting information from large databases is serious problem, which occurs in many fields e.g.: bioinformatics, commercial behaviour of Internet users, social networks analysis, management and investigation of various databases in static or dynamic states. In recent years many techniques discovering hidden structures in the data set like clustering and projection of data from high-dimensional spaces have been developed. In this paper, we propose a model for multiple view unsupervised clustering based on Kohonen self-organizing-map algorithm. The results of simulations in two dimensional space using three views of training sets having different statistical properties have been presented.


Spectral Cluster Multiple View Multiple Representation Data Mining Application Consensus Pattern 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tomasz Gałkowski
    • 1
  • Artur Starczewski
    • 1
  1. 1.Department of Computer EngineeringCzęstochowa University of TechnologyCzęstochowaPoland

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