Carnegie Mellon University
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Clustering with Interactive Feedback

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journal contribution
posted on 2012-10-01, 00:00 authored 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.


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