@misc{Zhong_Ning_A, author={Zhong, Ning and Skowron, Andrzej}, howpublished={online}, publisher={Zielona Góra: Uniwersytet Zielonogórski}, language={eng}, abstract={The knowledge discovery from real-life databases is a multi-phase process consisting of numerous steps, including attribute selection, discretization of real-valued attributes, and rule induction. In the paper, we discuss a rule discovery process that is based on rough set theory.}, abstract={The core of the process is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules from databases with uncertain and incomplete data. The system is based on a combination of Generalization Distribution Table (GDT) and the Rough Set methodologies.}, abstract={In the preprocessing, two modules, i.e. Rough Sets with Heuristics (RSH) and Rough Sets with Boolean Reasoning (RSBR), are used for attribute selection and discretization of real-valued attributes, respectively. We use a slope-collapse database as an example showing how rules can be discovered from a large, real-life database.}, type={artykuł}, title={A rough set-based knowledge discovery process}, keywords={rough sets, KDD process, hybrid systems}, }