Are there data clustering algorithms which by definition require human judgement as part of the algorithm? What I mean is that once in a while the algorithm presents the human operator (or any other species of operator) with a query of whether some objects (not necessarily from the original data set) should be grouped into the same cluster. The human operator serves as an oracle for the algorithm, and drives it towards a meaningful clustering.
It seems that such algorithms should exists, because different clustering algorithms usually optimize different target functions which depends on parameters, and on the one hand the user should choose an algorithm and parameters based on her goal, but on the other hand it is common with clustering problems that the user doesn't know in advance what he is looking for.
I tried Google searching for such algorithms, using key words that I thought might be related (for example, "human in the loop", "oracle", "agent", "semi supervised", "human judgment"), but so far I haven't found anything.