Exploration of Statistical and Textual Information by Means of Self-Organizing Maps

The self-organizing map (SOM) converts statistical relationships between highdimensional data into geometric relationships on a low-dimensional grid. It can thus be regarded as a projection and a similarity graph of the primary data. As it preserves the most important topological relationships of the data elements on the display, it may be thought of as producing some form of abstraction. These two aspects, visualization and abstraction, can be utilized in data mining, process analysis, machine perception, and organization of document collections.

[1]  Kimmo Kiviluoto,et al.  Predicting bankruptcies with the self-organizing map , 1998, Neurocomputing.

[2]  Samuel Kaski,et al.  Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997 , 1998 .

[3]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[4]  Kaisa Sere,et al.  Analyzing financial performance with self-organizing maps , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[5]  Christopher M. Bishop,et al.  GTM: A Principled Alternative to the Self-Organizing Map , 1996, NIPS.