Python: Deeper Insights into Machine Learning

Self-organizing maps

A SOM is a technique to generate topological representations of data in reduced dimensions. It is one of a number of techniques with such applications, with a better-known alternative being PCA. However, SOMs present unique opportunities, both as dimensionality reduction techniques and as a visualization format.

SOM – a primer

The SOM algorithm involves iteration over many simple operations. When applied at a smaller scale, it behaves similarly to k-means clustering (as we'll see shortly). At a larger scale, SOMs reveal the topology of complex datasets in a powerful way.

An SOM is made up of a grid (commonly rectangular or hexagonal) of nodes, where each node contains a weight vector that is of the same dimensionality as the ...