Self-organizing map for clustering in the graph domain

  • Authors:
  • Simon Günter;Horst Bunke

  • Affiliations:
  • Department of Computer Science, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland;Department of Computer Science, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland

  • Venue:
  • Pattern Recognition Letters - In memory of Professor E.S. Gelsema
  • Year:
  • 2002

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Abstract

Self-organizing map (som) is a flexible method that can be applied to various tasks in pattern recognition. However it is limited in the sense that it uses only pattern representations in terms of feature vectors. It was only recently that an extension to strings was proposed. In the present paper we go a step further and present a version of som that works in the domain of graphs. Graphs are a powerful data structure that include pattern representations based on strings and feature vectors as special cases. After introducing the new method a number of experiments will be described demonstrating its feasibility in the context of a graph clustering task.