A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Rough set theory (RST) is one of the data mining tools, which have many capabilities such as to minimize the size of an input data and to produce sets of decision rules from a set of data. RST is also one of the great techniques used in dealing with ambiguity and uncertainty of datasets. It was introduced by Z. Pawlak in 1997 and until now, there are many researchers who really make use of its advantages either to make an enhancement of the RST or to apply in various research areas such as in decision analysis, pattern recognition, machine learning, intelligent systems, inductive reasoning, data preprocessing, knowledge discovery, and expert systems. This paper presents a recent study on the elementary concepts of RST and its implementation in the multi-criteria decision analysis (MCDA) problems.