Abstract
Nowadays rate of growth of data from various applications of resources is increasing exponentially. The collections of different data sets are formulated into Big Data. The data sets are so complex and large in volume. It is very difficult to handle with the existing Database Management tools. Soft computing is an emerging technique in the field of study of computational intelligence. It includes Fuzzy Logic, Neural Networks, Genetic Algorithm, Machine Learning and Rough Set Theory etc. Rough set theory is a tool which is used to derive knowledge from imprecise, imperfect and incomplete data. This paper presents an evaluation of rough set theory applications to data mining techniques. Some of the rough set based systems developed for data mining such as Generalized Distribution Table and Rough Set System (GDTRS), Rough Sets with Heuristics (RSH), Rough Sets and Boolean Reasoning (RSBR), Map Reduce technique and Dynamic Data Mining etc. are analyzed. Models proposed and techniques employed in the above methods by the researchers are discussed .
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