Editing Self-organizing map (section)

=== Alternative algorithm ===
# Randomize the map's nodes' weight vectors
# Traverse each input vector in the input data set
## Traverse each node in the map
### Use the [[Euclidean distance]] formula to find the similarity between the input vector and the map's node's weight vector
### Track the node that produces the smallest distance (this node is the best matching unit, BMU)
## Update the nodes in the neighborhood of the BMU (including the BMU itself) by pulling them closer to the input vector
### <math>W_{v}(s + 1) = W_{v}(s) + \theta(u, v, s) \cdot \alpha(s) \cdot (D(t) - W_{v}(s))</math>
# Increase <math>s</math> and repeat from step 2 while <math>s < \lambda</math>