  We present an efficient and robust approach for extracting clusters of
galaxies from weak lensing survey data and measuring their properties. We use
simple, physically-motivated cluster models appropriate for such sparse, noisy
data, and incorporate our knowledge of the cluster mass function to optimise
the detection of low-mass objects. Despite the method's non-linear nature, we
are able to search at a rate of approximately half a square degree per hour on
a single processor, making this technique a viable candidate for future
wide-field surveys. We quantify, for two simulated data-sets, the accuracy of
recovered cluster parameters, and discuss the completeness and purity of our
shear-selected cluster catalogues.
