This article discusses methods that combine rough set theory with Boolean reasoning, focusing on their applications in pattern recognition, machine learning, data mining, and conflict analysis. The authors emphasize the importance of discernibility relations in rough set theory for constructing entities such as reducts, decision rules, and association rules. They introduce the concept of Boolean reasoning, which involves constructing Boolean functions to decode solutions for problems. The article also covers attribute selection, value set reduction (discretization and symbolic attribute value grouping), and the generation of minimal decision rules. Examples are provided to illustrate these concepts and techniques, highlighting their effectiveness in various data mining tasks. The methodology is supported by software systems like RSES and ROSETTA, and the authors discuss the challenges and limitations of the approaches, emphasizing the need for efficient heuristics to handle large datasets.This article discusses methods that combine rough set theory with Boolean reasoning, focusing on their applications in pattern recognition, machine learning, data mining, and conflict analysis. The authors emphasize the importance of discernibility relations in rough set theory for constructing entities such as reducts, decision rules, and association rules. They introduce the concept of Boolean reasoning, which involves constructing Boolean functions to decode solutions for problems. The article also covers attribute selection, value set reduction (discretization and symbolic attribute value grouping), and the generation of minimal decision rules. Examples are provided to illustrate these concepts and techniques, highlighting their effectiveness in various data mining tasks. The methodology is supported by software systems like RSES and ROSETTA, and the authors discuss the challenges and limitations of the approaches, emphasizing the need for efficient heuristics to handle large datasets.