A self-organising map (SOM) is a form of unsupervised learning where nodes on a 2D grid (or more geometric lattice structure) act as archetypes or key values for a form of clustering, where like items are allocated to grid points that have some form of relationship. The prime example of a SOM are Kohonen networks. Like other forms of clustering algorithm, SOM can be used for data interpretation where a human expert labels areas on the map, or as a form of {[dimension reduction}} either using the nodes as discrete categorical clusters or using the 2D (or higer diennsional) embedding of the grid or lattice as continuous dimensions.
Used on Chap. 5: page 88; Chap. 6: pages 109, 121, 124; Chap. 16: page 377; Chap. 21: page 527
Also known as self-organizing map, self-organising network, self-organizing network