Visualising Clusters in Self-Organising Maps with Minimum Spanning Trees.

R. Mayer, A. Rauber:
"Visualising Clusters in Self-Organising Maps with Minimum Spanning Trees.";
Vortrag: International Conference on Artificial Neural Networks (ICANN'10), Thessaloniki, Greece; 15.09.2010 - 18.09.2010; in:"Proceedings of the International Conference on Artificial Neural Networks (ICANN'10)", Springer-Verlag Berlin, Heidelberg (2010), ISBN: 3-642-15821-8; S. 426 - 431.

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Abstract:


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.