The chapter is focused on the data mining aspect of the applications of rough set theory.
Consequently, the theoretical part is minimized to emphasize the practical application side …

Since introduction of the theory of rough set in early eighties, considerable work has been
done on the development and application of this new theory. The paper provides a review of …

Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and
has been applied to many real-world problems including data mining, pattern recognition …

Along the years, rough set theory has earned a well-deserved reputation as a sound
methodology for dealing with imperfect knowledge in a simple though mathematically sound …

Data mining (DM) can be perceived as a methodology for discovering hidden patterns in
data. DM is a relatively new area of research and applications, stretching, over many …

Rough set data analysis (RSDA) was introduced to Computer Science in the early 1980s by
Z. Pawlak [Paw82] and has since come into focus as an alternative to the more widely used …

Rough sets extends classical set theory by incorporating the set model into the notion of
classification in the form of an indiscernibility relation. Rough sets serves as a tool for data …

ABSTRACT The Rough Set (RS) theory can be considered as a tool to reduce the input
dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there …

The package RoughSets, written mainly in the R language, provides implementations of
methods from the rough set theory (RST) and fuzzy rough set theory (FRST) for data …

Most likely they refer to the number of possible different cases (rows) of the table. They are
mistaken. The correct number is 2010^ 10= 1.024* 10^ 13). In all of these areas we may …