This chapter presents the Boolean reasoning approach to problem solving and its applications in Rough sets. The Boolean reasoning approach has become a powerful tool for designing effective and accurate solutions for many problems in decision-making, approximate reasoning and optimization. In recent years, Boolean reasoning has become a recognized technique for developing many interesting concept approximation methods in rough set theory. This chapter presents a general framework for concept approximation by combining the classical Boolean reasoning method with many modern techniques in machine learning and data mining. This modified approach - called “the approximate Boolean reasoning” methodology - has been proposed as an even more powerful tool for problem solving in rough set theory and its applications in data mining. Through some most representative applications in many KDD problems including feature selection, feature extraction, data preprocessing, classification of decision rules and decision trees, association analysis, the author hopes to convince that the proposed approach not only maintains all the merits of its antecedent but also owns the possibility of balancing between quality of the designed solution and its computational time.