Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
Self-organizing map network as an interactive clustering tool—an application to group technology
Decision Support Systems - Special issue on WITS '92
Self-organizing maps
Information Systems Research
Intelligent physician segmentation and management based on KDD approach
Expert Systems with Applications: An International Journal
Clustering the ecological footprint of nations using Kohonen's self-organizing maps
Expert Systems with Applications: An International Journal
FHC: The fuzzy hyper-prototype clustering algorithm
International Journal of Knowledge-based and Intelligent Engineering Systems - Intelligent Information Processing: Techniques and Applications
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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.