I have a data set where certain rows are labeled as one class (and interpreted as distinct cluster #1 as such), but other points are either unlabeled or ambiguous. Hence I want to figure out which unlabeled data points lie farthest from cluster #1 by sorting them by their respective distance from cluster #1 (more precisely, from the closest point of cluster #1 to the respective unlabeled points).
My first idea would to create a similarity matrix between and calculate the closest distances per unlabeled points from this, but somehow this seems a but clumsy, is there a more elegant/effective way?
(I used to use sklearn for similar tasks, but as far as I know, unsupervised clustering algos don't explicitly provide this kind of specific information.)