Rough Set Theory and its Extended Algorithms

Rough set theory is an algorithm which deals with an impreciseness and lack of certainty based on decision making. The theory gives us a practical way to extract the correct patterns from given data. The rough set theory has lots of interesting and useful applications. Rough set theory handles data reduction, generates precise decision rules very well and also not hard to understand. The Rough set theory deals with lower approximations, upper approximations and it's boundary region. The rough set approach is by all accounts of major significance to AI (Artificial Intelligence) and inductive reasoning, mostly in the areas of machine learning, decision analysis, pattern extraction, knowledge acquisition and pattern recognition This paper aims for rough set theory, explained with example and its extended algorithms with its applications which are used in area of data mining. Also, different experiments are performed which lead to significant conclusions.