Suppose one has trained a SOM on a certain number of data. Without explaining all the procedure, one can say that the SOM algorithm produces a certain number of prototypes and the new elements coming in input are clustered based on the distance from the prototypes.
Two possible packages are:
- kohonen::som (R)
- somclu (Python)
Here it is explained the fact that in a high dimensional context the euclidean distance is not the best to capture the difference among vectors. Nevertheless, relying on the two previous algorithms it seems (coincidence?) that there's not the possibility to choose a distance different from the euclidean one in order to train the models.
There exists a reason for which the euclidean distance could be the best (or only possible) one on the training process of a self organizing map?