Discovering Knowledge From Information Systems And Fuzziness Based On Covering Generalized Rough Sets

This paper discusses the meta-theory based on modal logic and its applications of interpreting and extracting decision rules from information systems, the measure of fuzziness based on covering generalized rough sets. The main contents of this paper are as follows: In chapter 2 and 3 , applications of meta-theory based on modal logic are studied which are interpreting and extracting decision rules from several different information systems. The whole paper is structured as follows.In chapter 2, basic knowledge about modal logic and meta-theory is introduced; the interpretation of decision table based on the meta-theory from fuzzy information system and vague information system are discussed, where granules of knowledge representation are obtained.In chapter 3, a fuzzy inference methods is obtained, namely inference of fuzzy decision rules based on fuzzy truth value restriction.In chapter 4, a measure of fuzziness based on covering generalized rough sets is proposed and some characters of mis measure are discussed. A measure of fuzziness based on positive-negative region covering generalized rough sets is also put forward. Using a specific example, visual analysis of the new definition is given.