Rough computing - A review of abstraction, hybridization and extent of applications

The rapid growth of information and communication technology captured common man and various organizations and influenced each individual's life, work, and study. It leads to a data explosion. It has no utility without any analysis and leads to many analytical techniques. The prime objective of these techniques is to derive some useful knowledge. However, the transformation of data into knowledge is not easy because of many reasons, such as disorganized, incomplete, uncertainties, etc. Furthermore, analyzing uncertainties present in data is not a straight forward task. Many different models, like fuzzy sets, rough sets, soft sets, neural networks, generalizations, and hybrid models obtained by combining two or more of these models, have been fruitful in representing knowledge. To this end, this paper identifies the conventionally used rough computing techniques and discusses their concepts, developments, abstraction, hybridization, and scope of applications.