Self-organizing map for clustering in the graph domain
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.
self-organizing map graph clustering structural pattern recognition graph matching graph edit distance neuron utility