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 methods 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.
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