document: Fast discovery of unexpected patterns in data , relative to a Bayesian network We consider a model in which background knowledge on a given domain of interest is available in terms of a Bayesian network , in addition to a large database . The mining problem is to discover unexpected patterns : our goal is to find the strongest discrepancies between network and database . This problem is intrinsically difficult because it requires inference in a Bayesian network and processing the entire , potentially very large , database . A sampling-based method that we introduce is efficient and yet provably finds the approximately most interesting unexpected patterns . We give a rigorous proof of the method 's correctness . Experiments shed light on its efficiency and practicality for large-scale Bayesian networks and databases .
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