Rough Set Theory — Fundamental Concepts, Principals, Data Extraction, and Applications

Abstract

This study, it has discussed the Rough set theory, was proposed in 1982 by Z. Pawlak, as an approach to knowledge discovery from incomplete, vagueness and uncertain data. The rough set approach to processing of incomplete data is based on the lower and the upper approximation, and the theory is defined as a pair of two crisp sets corresponding to approximations. The main advantage of rough set theory in data analysis is that it does not need any preliminary or additional information concerning data, such as basic probability assignment in Dempster-Shafer theory, grade of membership or the value of possibility in fuzzy set theory. The Rough Set approach to analysis has many important advantages such as (Pawlak, 1997): Finding hidden patterns in data; Finds minimal sets of data (data reduction); Evaluates significance of data; Generates sets of decision rules from data; Facilitates the interpretation of obtained result Different problems can be addressed though Rough Set Theory, however during the last few years this formalism has been approached as a tool used with different areas of research. There has been research concerning be relationship between Rough Set Theory and the Dempster-Shafer Theory and between rough sets and fuzzy sets. Rough set theory has also provided the necessary formalism and ideas for the development of some propositional machine learning systems. Rough set has also been used for knowledge representation; data mining; dealing with imperfect data; reducing knowledge representation and for analyzing attribute dependencies. Rough set Theory has found many applications such as power system security analysis, medical data, finance, voice recognition and image processing; and one of the research areas that has successfully used Rough Set is the knowledge discovery or Data Mining in database.