Knowledge Discovery Based On Interval Intuitionistic Fuzzy Information Systems

Knowledge discovery is an important part of the artificial intelligence field. Rough settheory provides a new method for knowledge acquisition in complex systems. At present,researchers discussed about knowledge reduction and rule extraction in various complete orincomplete information systems, such as classic Pawlak information systems, fuzzyinformation system, random information systems, scale-value information systems. With thedevelopment of rough set theory, a natural topic that interval-valued intuitionistic fuzzy setsand rough sets theory are combined and applied into uncertain system of knowledgediscovery and knowledge processing is generated. In this thesis, the knowledge discovery forinterval-valued intuitionistic fuzzy information system is studied, the specific contents are asfollows.(1)First, the concept of inclusion degree is applied into the knowledge acquisition ininterval-valued intuitionistic fuzzy information systems. The concept and formula of inclusiondegree of Interval Intuitionstic fuzzy set are proposed, according to the max-inclusion degree,maximum coordination reduction is given then the rule extraction. Second, incompleteinterval-valued intuitionistic fuzzy decision information systems is established and thedefinitions of its rough approximation are given. The definitions of precision reduction areshowed,and concrete method of iscernibility matrix, iscernibility function, rule extraction areintroduced.Through a simple experiment, the application of the algorithm is rationality andvalidity.(2)For the interval-valued intuitionistic fuzzy decision information system with noise,the variable precision rough sets and interval-valued intuitionistic fuzzy sets are combined.For the rough interval-valued intuitionistic fuzzy decision table, and the outputinterval-valued intuitionistic fuzzy sets are changed into common sets by using cut sets, therough membership function is definited based on this, and variable precision roughinterval-valued intuitionistic fuzzy model is established through the confidence regions, theapproximate reduction of rough interval-valued intuitionistic fuzzy decision table are given,and the method that probability decision rule be obtained is established.(3)Introducing the interval-valued intuitionistic fuzzy rough set into logic reasoning,toexpand the semantic reasoning of classical fuzzy logic. Firstly, fives logic values in theinterval-valued intuitionstic fuzzy proposition logic are given including interval-valuedintuitionistic fuzzy true, interval-valued intuitionistic fuzzy false, interval-valued intuitionisticfuzzy rough true, interval-valued intuitionistic fuzzy rough false and interval-valuedintuitionistic fuzzy rough incompatible; and then it mainly discusses the semantic ofinterval-valued intuitionistic fuzzy rough proposition formulas in approximate space;intuitonistic fuzzy rough logic is discussed on the basic of the interval-valued intuitionsticfuzzy rough semantic logic; finally, the semantic reasoning methods with various logic formulas are given.