Clustering with Interactive Feedback
journal contributionposted on 01.10.2012 by Maria-Florina Balcan, Avrim Blum
Any type of content formally published in an academic journal, usually following a peer-review process.
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.