Chinese Journal of Computers   Full Text
Title	A Survey on Rough Set Theory and Applications
Authors	WANG Guo-Yin1) YAO Yi-Yu2) YU Hong1),2)
Address	1)(Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065) 2)(Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada S4S 0A2)
Year	2009
Issue	No.7(1229—1246)
Abstract &   Background	Abstract This paper presents a framework for a systematic study of the rough set theory. Various views and interpretations of the theory and different approaches to study the theory are discussed. The relationships between the rough sets and other theories, such as fuzzy sets, evidence theory, granular computing, formal concept analysis, knowledge spaces, etc., are examined. The paper also reviews recent theoretic studies and applications of rough sets and points out future research directions. Keywords rough sets; fuzzy sets; granular computing; formal concept analysis; knowledge spaces; intelligent information processing Background The advances of computer technology have led to an unprecedented power in data collection, processing, management and storage. A grand challenge is how to transform data into information, information into knowledge, and knowledge into wisdom. For this purpose, many theories, models, methods and tools have been proposed and extensively studied. Rough set theory, proposed in the early 1980s, is a mathematical theory and tool for dealing with imprecise, incomplete, uncertain, and vague information. It has been applied successfully in many fields such as decision analysis, machine learning, data mining, knowledge discovery, pattern recognition, etc. This paper provides a review of the main theoretical developments and applications of rough set theory. The authors introduce a framework for a systematic study of the rough set theory, discuss various views of the theory and different approaches to study the theory. The authors summarize the relationships between the rough set theory and other theories of data analysis, such as fuzzy sets, evidence theory, granular computing, formal concept analysis, knowledge spaces, etc, and point out possible future research and development trends in rough set theory. This work is partially supported by the National Natural Science Foundation of China (Nos.60573068 and 60773113), Natural Science Foundation of Chongqing of China (Nos.2008BA2017 and 2008BA2041), Science & Technology Research Program of the Municipal Education Committee of Chongqing of China (No.KJ080510).