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 …

In 1982, Pawlak proposed rough set theory to formalize imprecise and uncertain data and
reasoning from data. In rough set theory, objects are grouped together by their features as a …

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 …

This paper summarizes various formulations of the standard rough set theory. It
demonstrates how those formulations can be adopted to develop different generalized …

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 was proposed by Zdzislaw Pawlak (1982, 1991) in the early 1980s. Since
then we have witnessed a systematic, worldwide growth of interest in rough set theory and …

A generalization of the original idea of rough sets as introduced by Pawlak is presented. The
generalization, called the Variable Precision Rough Sets Model with Asymmetric Bounds, is …

Data uncertainty is common in real-world applications due to various causes, including
imprecise measurement, network latency, out-dated sources and sampling errors. As a …

Rough set theory is a new method that deals with vagueness and uncertainty emphasized in
decision making. Data mining is a discipline that has an important contribution to data …

Knowledge Discovery in Databases (KDD) has evolved into an important and active area of
research because of theoretical challenges and practical applications associated with the …