Clustering with Interactive Feedback

2012-10-01T00:00:00Z (GMT) by Maria-Florina Balcan Avrim Blum
In this paper, we initiate a theoretical study of the problem of clustering data under interactive feedback.We introduce a query-based model in which users can provide feedback to a clustering algorithm in a natural way via split and merge requests. We then analyze the “clusterability” of different concept classes in this framework — the ability to cluster correctly with a bounded number of requests under only the assumption that each cluster can be described by a concept in the class — and provide efficient algorithms as well as information-theoretic upper and lower bounds.