Self Organizing Maps by Giuseppe Vettigli.
From the post:
The Self Organizing Maps (SOM), also known as Kohonen maps, are a type of Artificial Neural Networks able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. In a SOM the neurons are organized in a bidimensional lattice and each neuron is fully connected to all the source nodes in the input layer. An illustration of the SOM by Haykin (1996) is the following
If you are looking for self organizing maps using Python, this is the right place.
As with all mathematical techniques, SOMs requires the author to bridge the gap between semantics and discrete values for processing.
An iffy process at best.