AN APPROACH TO SOLVE SOME UNSOLVED LIMITATIONS: ROUGH SET THEORY

Rough set theory is a mathematical approach to dealing with uncertainty and vagueness in data. It was introduced as a way to approximate classical sets using lower and upper bounds. Rough set theory has been applied to various domains such as data mining, knowledge discovery, machine learning, soft computing, medical analysis, synthesis of switching circuits, and civil engineering. Rough set theory can handle imprecise and noisy data by finding structural relationships and dependencies among attributes. It can also reduce redundant and irrelevant attributes and generate decision rules from data. Rough set theory is closely related to fuzzy set theory, but differs in that it uses multiple memberships instead of partial memberships to model uncertainty. Lot of research works have taken place in this domain with many fruitful outcomes that helped lot in expanding this field to much wider reach of knowledge irrespective of the domain concerned to mathematical and technological application development. In lieu of such research-oriented progress in different versatile domain, many areas of research has remained untouched as many a problem remained unsolved being shrouded under the mystery. This paper deals mainly with some of the still unsolved questions on Rough Set that are under study for new discovery and a way to overcome such limitations citing proper techniques, examples, working models, and graphs.