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How to make large self-organizing maps for nonvectorial data
1Neural Networks Research Centre, Helsinki University of Technology, Finland. teuvo.kohonen@hut.fi
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.