Data topology visualization for the Self-Organizing Map
Kadim Taşdemi̇r, Erzsébet Merényi · The European Symposium on Artificial Neural Networks · 2006
The Self-Organizing map (SOM), a powerful method for data mining and cluster extraction, is very useful for processing data of high dimensionality and complexity. Visualization methods present different aspects of the information learned by the SOM to gain insight and guide segmentation of the data. In this work, we propose a new visualization scheme that represents data topology superimposed on the SOM grid, and we show how it helps in the discovery of data structure.