In simulation-based functional verification, composing and debugging testbenches can be tedious and timeconsuming. A simulation-based data-mining approach  was proposed as an alternative for functional test pattern generation. However, the core of the approach is in solving Boolean learning, which is the problem of learning Boolean functions from bit-level simulation data. In this paper, an efficient data mining engine based on novel decision-diagram(DD) based learning approaches is presented. We compare the DD-based learning approaches to other known methods such as Nearest Neighbor and Support Vector Machine. We show that the new Boolean data miner is efficient for practical use and the learned results can provide compact and accurately approximate representations of Boolean functions. Finally, we show that the proposed methodology incorporated with the current Boolean data miner can achieve a high fault coverage (95.36%) on the OpenRISC 1200 microprocessor, demonstrating the effectiveness of our approach.