Self-organizing map (SOM), or Kohonen Map, is a computational data analysis method which produces nonlinear mappings of data to lower dimensions. Alternatively, the SOM can be viewed as a clustering algo- rithm which produces a set of clusters organized on a regular grid. The roots of SOM are in neural compu- tation; it has been used as an abstract model for the formation of ordered maps of brain functions, such as sensory feature maps. Several variants have been pro- posed, ranging from dynamic models to Bayesian variants. The SOM has been used widely as an engineering tool for data analysis, process monitoring, and informa- tion visualization, in numerous application areas.