Semi-supervised Clustering with Example Clusters
Celine Vens, Bart Verstrynge, Hendrik Blockeel · 2013
We consider the following problem: Given a set of data and one or more examples of clusters, find a clustering of the whole data set that is consistent with the given clusters. This is essentially a semi-supervised clustering problem, but different from those that have been studied until now. We argue that it occurs frequently in practice, but despite this, none of the existing methods can handle it well. We present a new method that specifically targets this type of problem. We show that the method works better than standard methods and identify opportunities for further improvement.