Extending the Kohonen self-organizing map networks for clustering analysis

Abstract

The self-organizing map (SOM) network was originally designed for solving problems that involve tasks such as clustering, visualization, and abstraction. While Kohonen's SOM networks have been successfully applied as a classification tool to various problem domains, their potential as a robust substitute for clustering and visualization analysis remains relatively unresearched. We believe the inadequacy of attention in the research and application of using SOM networks as a clustering method is due to its lack of procedures to generate groupings from the SOM output. In this paper, we extend the original Kohonen SOM network to include a contiguity-constrained clustering method to perform clustering based on the output map generated by the network. We compare the result with that of the other clustering tools using a classic problem from the domain of group technology. The result shows that the combination of SOM and the contiguity-constrained clustering method produce clustering results that are comparable with that of the other clustering methods. We further test the applicability of the method with two widely referenced machine-learning cases and compare the results with that of several popular statistical clustering methods.