Validity Study of Kohonen Self-Organizing Maps Huh, Myung-Hoe;
Self-organizing map (SOM) has been developed mainly by T. Kohonen and his colleagues as a unsupervised learning neural network. Because of its topological ordering property, SOM is known to be very useful in pattern recognition and text information retrieval areas. Recently, data miners use Kohonen´s mapping method frequently in exploratory analyses of large data sets. One problem facing SOM builder is that there exists no sensible criterion for evaluating goodness-of-fit of the map at hand. In this short communication, we propose valid evaluation procedures for the Kohonen SOM of any size. The methods can be used in selecting the best map among several candidates.
Kohonen Self-Organizing Map (SOM);Data Mining;Partitioned Data Sets;Valid Measure of Lack-of-Fit;Re-sampling;
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