Discovery through rough set theory
Wojciech Ziarko · Communications of the ACM · 1999
The article discusses some representative applications of Rough Sets technology using data-logic and other tools. Most of these applications fall into categories such as market research, medicine, control, drug and new material design research, stock market, pattern recognition and environmental engineering. Applications of rough sets theory to knowledge discovery involve collecting empirical data and building classification models from the data. The main distinction in this approach is it's primarily concerned with the acquisition of decision tables from data followed by their analysis and simplification by identifying attribute dependencies, minimal non-redundant subsets of attributes, most important attributes and minimized rules. The technology of rough sets has been applied to practical knowledge discovery problems since late 1980s. All applications of the rough sets methodology developed since its inception can be called knowledge discovery applications. The availability of advanced development tools and increased familiarity with the merits of the methodology will result in substantial growth in the number and quality of applications based on knowledge extracted from data.