Add to ORKGRough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. This paper rewrites some properties of rough relations found in the literature, proving their validity
Rough set theory is a new mathematical approach to data analysis. The rough set ap-proach seems to b...
Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision ma...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...
Rough set theory (RST) focuses on forming posets of equivalence relations to describe sets with incr...
In this paper rudiments of the theory will be outlined, and basic concepts of the theory will be ill...
Rough set theory (RST), since its introduction in Pawlak (1982), continues to develop as an effectiv...
Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been appl...
Abstract. Rough sets have traditionally been applied to decision (classification) problems. We sugge...
Rough set theory is a new mathematical approach to vagueness and uncertainty. The theory has found m...
Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research bec...
Abstract- Rough set theory has emerged as a useful mathematical tool to extract conclusions or decis...
Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for ...
The theory of rough sets is an extension of set theory with two additional unary set-theoretic opera...
Rough set theory (RST) involves techniques for knowledge discovery or data mining. RST is typically ...
The Rough Set Theory was proposed by Pawlak, in 1982, as a mathematical model to represent knowledge...
Rough set theory is a new mathematical approach to data analysis. The rough set ap-proach seems to b...
Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision ma...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...
Rough set theory (RST) focuses on forming posets of equivalence relations to describe sets with incr...
In this paper rudiments of the theory will be outlined, and basic concepts of the theory will be ill...
Rough set theory (RST), since its introduction in Pawlak (1982), continues to develop as an effectiv...
Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been appl...
Abstract. Rough sets have traditionally been applied to decision (classification) problems. We sugge...
Rough set theory is a new mathematical approach to vagueness and uncertainty. The theory has found m...
Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research bec...
Abstract- Rough set theory has emerged as a useful mathematical tool to extract conclusions or decis...
Rough set theory (RST) offers an interesting and novel approach both to the generation of rules for ...
The theory of rough sets is an extension of set theory with two additional unary set-theoretic opera...
Rough set theory (RST) involves techniques for knowledge discovery or data mining. RST is typically ...
The Rough Set Theory was proposed by Pawlak, in 1982, as a mathematical model to represent knowledge...
Rough set theory is a new mathematical approach to data analysis. The rough set ap-proach seems to b...
Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision ma...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...