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R. Mayer, A. Rauber:
"Visualising Clusters in Self-Organising Maps with Minimum Spanning Trees.";
Talk: International Conference on Artificial Neural Networks (ICANN '10), Thessaloniki, Greece; 2010-09-15 - 2010-09-18; in: "Proceedings of the International Conference on Artificial Neural Networks (ICANN '10)", Springer-Verlag Berlin, Heidelberg (2010), ISBN: 3-642-15821-8; 426 - 431.

The Self-Organising Map (SOM) is a well-known neural-network model that has successfully been used as a data analysis tool in many different domains. The SOM provides a topology-preserving mapping from a high-dimensional input space to a lower-dimensional output space, a convenient interface to the data. However, the real power of this model can only be utilised with sophisticated visualisations that provide a powerful tool-set for exploring and understanding the characteristics of the underlying data. We thus present a novel visualisation technique that is able to illustrate the structure inherent in the data. The method builds on minimum spanning trees as a graph of similar data items, which is subsequently visualised on top of the SOM grid.

http://publik.tuwien.ac.at/files/PubDat_191196.pdf