Talks and Poster Presentations (with Proceedings-Entry):
R. Mayer, A. Rauber:
"Visualising Clusters in Self-Organising Maps with Minimum Spanning Trees.";
Talk: International Conference on Artificial Neural Networks (ICANN '10),
- 2010-09-18; in: "Proceedings of the International Conference on Artificial Neural Networks (ICANN '10)",
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.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.