Let's go through process of how SOMs learn:

  1.  Each node's weights are initialized by small standardized random values. These act like coordinates for different output nodes.
  2. The first row's input (taking the first row from all of the variables) is fed into the first node.
  3. Now, we have got two vectors. If V is the current input vector and W is the node's weight vector, then we calculate the Euclidean distance, like so:

  1. The node that has a weight vector closest to the input vector is tagged as the best-matching unit (BMU).
  2. A similar operation is carried out for all the rows of input and weight vectors. BMUs are found for all.
  3. Once the ...

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