  We propose a new clustering technique that can be regarded as a numerical
method to compute the proximity gestalt. The method analyzes edge length
statistics in the MST of the dataset and provides an a contrario cluster
detection criterion. The approach is fully parametric on the chosen distance
and can detect arbitrarily shaped clusters. The method is also automatic, in
the sense that only a single parameter is left to the user. This parameter has
an intuitive interpretation as it controls the expected number of false
detections. We show that the iterative application of our method can (1)
provide robustness to noise and (2) solve a masking phenomenon in which a
highly populated and salient cluster dominates the scene and inhibits the
detection of less-populated, but still salient, clusters.
