Clustering with interactive feedback

Abstract

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. © 2008 Springer-Verlag Berlin Heidelberg.