2010
NeurIPS
NeurIPS 2010
Rates of convergence for the cluster tree
Abstract
For a density f on R^d, a high-density cluster is any connected component of {x: f(x) >= c}, for some c > 0. The set of all high-density clusters form a hierarchy called the cluster tree of f. We present a procedure for estimating the cluster tree given samples from f. We give finite-sample convergence rates for our algorithm, as well as lower bounds on the sample complexity of this estimation problem.
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Authors
Topics
Machine Learning > Core Methods > Clustering
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Optimization & Theory > Statistics
Machine Learning > Optimization & Theory > Sample Complexity
Machine Learning > Core Methods > Density Estimation