A Clustering Method Using Hierarchical Self-Organizing Maps

We describe a method of clustering that uses self-organizing maps (SOMs) in a method of image classification. To ensure that this clustering method is fast, we defined a hierarchical SOM and used it to construct the clustering method (M. Endo, M. Ueno, T. Tanabe, and M. Yamamoto, in Proc. of the IEEE Int. Workshop on Neural Networks for Signal Processing X, 2000, pp. 261–270). We define the clustering method in detail and outline its behavior as determined on the basis of both theory and experiment. We also propose a cooperative learning algorithm for the hierarchical SOM. Experiments on artificial image data confirmed the basic performance and adaptability of the SOM in clustering images. We also confirmed, both experimentally and theoretically, that our method is faster SOM, for the objects used in these experiments, than a method based on a non-hierarchical SOM.

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