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Optimizing (ie. training) an estimator with input data will result in different results depending on the distribution of data points: wherever there is a high density of data points, the optimizer will reduce the error there, possibly incurring greater error where the density of data points is lower.

There is no cost function that the SOM algorithm follows exactly.