Editing Self-organizing map (section)

== Overview ==
Self-organizing maps, like most artificial neural networks, operate in two modes: training and mapping. First, training uses an input data set (the "input space") to generate a lower-dimensional representation of the input data (the "map space"). Second, mapping classifies additional input data using the generated map.

In most cases, the goal of training is to represent an input space with ''p'' dimensions as a map space with two dimensions. Specifically, an input space with ''p'' variables is said to have ''p'' dimensions. A map space consists of components called "nodes" or "neurons", which are arranged as a [[hexagonal]] or [[rectangular]] grid with two dimensions.<ref>{{cite web |url=http://users.ics.aalto.fi/jhollmen/dippa/node9.html |author=Jaakko Hollmen |date=9 March 1996 |title=Self-Organizing Map (SOM) |website=[[Aalto University]]}}</ref> The number of nodes and their arrangement are specified beforehand based on the larger goals of the analysis and [[exploratory data analysis|exploration of the data]].

Each node in the map space is associated with a "weight" vector, which is the position of the node in the input space. While nodes in the map space stay fixed, training consists in moving weight vectors toward the input data (reducing a distance metric such as [[Euclidean distance]]) without spoiling the topology induced from the map space. After training, the map can be used to classify additional observations for the input space by finding the node with the closest weight vector (smallest distance metric) to the input space vector.