Custom Kmeans

[cveaux]

Hi, I think that there is an over-simplification in Custom Kmeans, the way the centroids are estimated:

centres[each_center] = np.mean(X[each_center_samples], axis=0)

doesn't actually yield to the point that minimise the average custom distances within a cluster.
The mean is the optimal solution for the euclidian distance but not for an arbitrary distance. For instance, in the case of cosine distance, the mean calculated as above will give the optimum center of the cluster only if X rows are l2-normalised.

A more general solution would be to use sklearn_extra.cluster.KMedoids