Considering Topology in the Clustering of Self-organizing Maps

– The Self-Organizing Map (SOM) [1] is an effective tool for clustering and data mining. One way to extract cluster structure from a trained SOM is by clustering its weights, which has great potential for automation. This potential is not fully realized by existing algorithms, and leaves large, high-dimensional, complex data to semi-manual treatment. Our main contribution is the exploitation of the data topology in clustering the SOM. Combined with appropriate distance and cluster validity measures, this results in a high degree of precision and automation of cluster extraction, including the discovery of rare clusters. It may work with prototypes of other quantization methods since direct use of SOM locations can be avoided.