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Som Input

Merge SOM for temporal data

The recent merging self-organizing map (MSOM) for unsupervised sequence processing constitutes a fast, intuitive, and powerful unsupervised learning model. In this paper, we investigate its theoretical and practical properties. Particular focus is put on the context established by the self-organizing MSOM, and theoretic results on the representation capabilities and the MSOM training dynamic ar...

3D SOM Neighborhood Algorithm

Neighborhood algorithm is an important part of 3D SOM algorithm. We proposed three kinds of neighborhood shape and two kinds of neighborhood decay function for threedimensional self-organizing feature maps (3D SOM) algorithm and applied them to three-dimensional image compression coding. Experimental results show that the 3D orthogonal cross neighborhood shape and exponential function algorithm...

SOM in data mining

Topology Preservation in SOM

The SOM has several beneficial features which make it a useful method for data mining. One of the most important features is the ability to preserve the topology in the projection. There are several measures that can be used to quantify the goodness of the map in order to obtain the optimal projection, including the average quantization error and many topological errors. Many researches have st...