Input one fixed cluster centroid, find N others (python)
Rather than recycling clustering for this, treat it as a regular optimization problem. You don't want to "discover structure", but optimize cost.
Beware that earth is not flat, and Euclidean distance (i.e. k-means) is a bad idea. 1 degree north is only at the equator approximately the same distance to 1 degree east. If your data is e.g. in New York, you have a non-neglibile distortion, and your solution will not even be a local optimum.
If you absolutely insist on abusing kmeans, it's easy to do.
Choose n-1 centers at random and the predefined one.
Then run 1 iteration of k-means only. Then replace that center with the desired center again. Repeat with the next iteration.
I have a table of shipment destinations in lat, long. I have one fixed origination point (also lat, long). I would like to find other optimal origin locations using clustering. In other words, I want to assign one cluster centroid (keep it fixed) and find 1, 2, 3 . . . N other cluster centroids. Is this possible with the scikit learn cluster module?