Abstract
Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-matrix is a commonly used technique to cluster the SOM visually. However, in order to be really useful, clustering needs to be an automated process. There are several techniques which can be used to cluster the SOM autonomously, but the results they provide do not follow the results of U-matrix very well. In this paper, a clustering approach based on distance matrices is introduced which produces results which are very similar to the U-matrix. It is compared to other SOM-based clustering approaches.
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Vesanto, J., Sulkava, M. (2002). Distance Matrix Based Clustering of the Self-Organizing Map. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_154
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DOI: https://doi.org/10.1007/3-540-46084-5_154
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