A comparative analysis of an extended SOM network and K-means analysis

The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The main function of SOM networks is to map the input data from an n-dimensional space to a lower dimensional plot while maintaining the original topological relations. In this research, we apply an extended SOM network that includes a grouping function to further cluster input data based on the relationships derived from a lower dimensional SOM map, to market segmentation problems. A computer program for implementing the extended SOM networks has been developed and it was first compared with K-means analysis in an experimental design using simulated data sets with known cluster solutions. Test results indicate that the extended SOM networks perform better when the data are skewed. We then further test the performance of the method with a real-world data set from a widely referenced machine-learning case. We believe the findings from this research can be applied to other problem domains as well.