SOM - Self organizing Map is a Swing application that implements the Self organizing map algorithm.
Self-organizing map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce low-dimensional representation of the training samples while preserving the topological properties of the input space. Self-Organizing Map showing US Congress voting patterns visualized in Synapse Self-Organizing Map showing US Congress voting patterns visualized in Synapse.
This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map.
Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.