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.
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Balcan, MF., Blum, A. (2008). Clustering with Interactive Feedback. In: Freund, Y., Györfi, L., Turán, G., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2008. Lecture Notes in Computer Science(), vol 5254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87987-9_27
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DOI: https://doi.org/10.1007/978-3-540-87987-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87986-2
Online ISBN: 978-3-540-87987-9
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