I'm working on a clustering problem. I have a training set composed of sets of points where the clusters are known and I want to find the good clusters on a testing dataset. It's a kind of supervised clustering.
I looked for articles about supervised clustering but I didn't find a lot of informations. There is "semi-supervised clustering" which consists of using informations on couples of points (must-link or don't-link relations) but, in my task, I don't have this kind of information. There are also some kind of "metric learning supervised clustering" which uses the labelized clusters to estimate a metric that would produce the given clusters using k-means. That kind of technique could help me but there is not much articles about it and I wonder if I'm not finding the good keywords or something.
What are the techniques/algorithms to cluster data points using labelized data (training points with known clusters) ?