Self Organizing Maps (SOMap)

A self-organizing map (SOM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.

(From Wikipedia, the free encyclopedia)

References

The original paper, that introduced this method is described in:

1. Kohonen, Teuvo (2013). "Essentials of the self-organizing map". Neural Networks. 37: 52–65. doi:10.1016/j.neunet.2012.09.018

Examples

Some (toy) examples on how to use this method, using sklearn datasets, can be found in:

1. Toy-Examples