We investigate the problem of generating fast approximate answers to queries posed to large sparse binary data sets. We focus in particular on probabilistic model-based approaches to this problem and develop a number of techniques that are significantly more accurate than a baseline independence model. In particular, we introduce two techniques for building probabilistic models from frequent it...

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