How to make large self-organizing maps for nonvectorial data

  • Authors:
  • Teuvo Kohonen;Panu Somervuo

  • Affiliations:
  • Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015 Hut, Finland;Neural Networks Research Centre, Helsinki University of Technology, P.O. Box 5400, FIN-02015 Hut, Finland

  • Venue:
  • Neural Networks - New developments in self-organizing maps
  • Year:
  • 2002

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Abstract

The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.