How many queries are needed to learn?
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Exact Learning of Discretized Geometric Concepts
SIAM Journal on Computing
Learning mixtures of arbitrary gaussians
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Machine Learning
Machine Learning
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
A spectral algorithm for learning mixture models
Journal of Computer and System Sciences - Special issue on FOCS 2002
On Learning Mixtures of Heavy-Tailed Distributions
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Spectral clustering by recursive partitioning
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A discriminative framework for clustering via similarity functions
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Isotropic PCA and Affine-Invariant Clustering
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
The spectral method for general mixture models
COLT'05 Proceedings of the 18th annual conference on Learning Theory
On spectral learning of mixtures of distributions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
Personalized collaborative clustering
Proceedings of the 23rd international conference on World wide web
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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 splitand mergerequests. 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.