Zhong, Ning; Skowron, Andrzej A rough set-based knowledge discovery process. (English) Zbl 0990.68139 Int. J. Appl. Math. Comput. Sci. 11, No. 3, 603-619 (2001). Summary: 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. 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 GDT and the Rough Set methodologies. In the preprocessing, two modules, i.e. RS with heuristics and RS with Boolean reasoning, 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. Cited in 8 Documents MSC: 68T30 Knowledge representation 68T05 Learning and adaptive systems in artificial intelligence 68P15 Database theory Keywords:knowledge discovery; real-life databases Software:RSBR_ PDFBibTeX XMLCite \textit{N. Zhong} and \textit{A. Skowron}, Int. J. Appl. Math. Comput. Sci. 11, No. 3, 603--619 (2001; Zbl 0990.68139) Full Text: EuDML