A Self Organizing Map (SOM) is a technique used for machine learning that produces a two-dimensional representation of a higher dimensional data set. The goals is to projects the data set into a lower dimension while preserving the original data structure. The data set is separated into clusters of variables with similar values which can be visualized in a map. In this way, the clusters that are near each other have more similar values than the clusters that are far away. This process makes high-dimensional data easier to visualize and also easier to analyse. A SOM is a type of artificial neural network but instead of learning from error-correction learning like backpropagation with gradient decent, it trains by using competitive learning.

There are many different applications of self-organizing maps but in this notebook we will focus on one that addresses the classification problem using the wine dataset we have used previously. The code found in this notebook is a derivative of the notebook from Minisom: https://github.com/JustGlowing/minisom/blob/master/examples/BasicUsage.ipynb used in accordance to its MIT License.