Rough Set Algebra And Rough Sets Attribute Reduction

Rough set theory,proposed by Pawlak.z in the early 1980s,is a new math tools to deal with fuzzy and uncertain information.It's basic mainidea is that through knowledge reduction,lead to classified rules of conception in the precondition of unchanging classification.Recently,rough set has already been one of the most active research areas in artificial intelligence and information science.And it successfully applied in the fields of date mining,pattern recognition,machine learning,knowledge discovery,decision analysis,and so on.The successful application of rough set in datebases promoted the research of rough set theory.The algebra character and algebra depiction of rough set was one of the most active branches in rough set theory research.Ms algebra,a kind of algebras,was abstracted from the commen characters of De Morgan algebra and Stone algebra.The method of constructing pseudo-complemented MS algebra from rough set algebra was presented.Based on the atoms and congruence relations of approximation algebra,it is proved that rough set algebra becomes pseudo-complemented MS algebra if proper complement operators are selected.In the application of rough set theory,the algorithm for attribute reduction is important,so it is always a focus of rough set theory. But in practical using,especially when the dates were inaccurate and uncertain,it is hard to select rules that which reducts lead to.So,it is necessary to find out all reducts. In this paper,a algorithm for attribute reduction based on discernibility matrix was introduced.The algorithm introduced the concept of stack.we use the feature of stack to improve the existed algorithm ,and propose a algorithm about all attribute reduction.By using the promoted reducts algorithm,we can get the valid rules.