Type of Neural network (unsupervised learning). SOM's goal is to convert high-dimensional data into low-dimensional, like 2D map, which can be understood by human. Impl. questions: neural network structure, learning parameters, performance, use, multi-thread optimization, etc.
A self-organizing map (SOM) or self-organizing feature map (SOFM) is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional), discretized representation of the input space of the training samples, called a map. Self-organizing maps are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space.
