actually refers to a cluster job on a much smaller data set and use those as the label and service supervisor set and apply this for the classes of all the others and see if those classes would actually fit in the initial foster class and whether it be actually faster than doing the big massive across from John just okay so so the idea would be to take a subset of the unlabeled data you're strictly unlabeled data take a subset train a clustering model on that and use the clusters as ground truth labels that sounds to me like a