Data topology visualization for the Self-Organizing Map

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.

[1]  Juha Vesanto,et al.  SOM-based data visualization methods , 1999, Intell. Data Anal..

[2]  Johan Himberg,et al.  A SOM based cluster visualization and its application for false coloring , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[3]  Andreas Rauber,et al.  Advanced Visualization Techniques for Self-organizing Maps with Graph-Based Methods , 2005, ISNN.

[4]  Thomas Martinetz,et al.  Topology representing networks , 1994, Neural Networks.

[5]  Anil K. Jain,et al.  A nonlinear projection method based on Kohonen's topology preserving maps , 1992, IEEE Trans. Neural Networks.

[6]  A. Ultsch.  Maps for the Visualization of high-dimensional Data Spaces , 2003 .

[7]  Erzsébet Merényi,et al.  Forbidden magnification? II , 2004, ESANN.

[8]  T. Villmann,et al.  Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis , 2001 .

[9]  Marie Cottrell,et al.  A Kohonen map representation to avoid misleading interpretations , 1996, The European Symposium on Artificial Neural Networks.

[10]  M. V. Velzen,et al.  Self-organizing maps , 2007 .