Rough Set Theory and its Applications in Data Mining

Abstract

One method for handling imprecise, ambiguous, and unclear data is rough set theory. Rough set theory offers a practical method for making decisions during data extraction. The practice of analyzing vast amounts of data to extract useful information from a larger collection of raw data is known as data mining. This paper discusses consistent data with rough set theory, covering blocks of attribute-value pairs, information table reductions, decision tables, and indiscernibility relations. It also explains the basics of rough set theory with a focus on applications to data mining. Additionally, rule induction algorithms are explained. The rough set theory for inconsistent data is then introduced, containing certain and potential rule sets along with lower and upper approximations. Finally, a presentation and explanation of rough set theory to incomplete data is given. This includes characteristic sets, characteristic relations, and blocks of attribute-value pairs.