Clustering with Interactive Feedback

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.

• Type: Conference
• Year: 2008
• Where: ALT
• Authors: Maria-Florina Balcan, Avrim Blum
• Keywords: ALT 2008, Clustering Algorithm, Different Concept Classes, Machine Learning, Query-based Model