Clustering with Confidence: A Low-Dimensional Binning Approach

We present a plug-in method for estimating the cluster tree of a density. The method takes advantage of the ability to exactly compute the level sets of a piecewise constant density estimate. We then introduce clustering with confidence, an automatic pruning procedure that assesses significance of splits (and so clusters) in the cluster tree; the only user input required is the desired confidence level.