Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering
Pattern Recognition Letters
Self-Organizing Maps
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Fuzzy clustering of the self-organizing map: some applications on financial time series
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Principles of employing a self-organizing map as a frequent itemset miner
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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Clustering of data is one of the main applications of the Self-Organizing Map (SOM). U-matrixis 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-matrixv ery 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 SOMbased clustering approaches.