Study Of Knowledge Reduction And Related Problems In Information System

Rough set theory is introduced by Professor Z.Pawlak in Warsaw university of technology, Poland, which is a research method of organization and analyze of incomplete, inaccurate or vague knowledge. Since it has been introduced, rough sets have been used widely in many fields, such as model recognition, data mining and decision analyze.Knowledge reduction is a kernel proble in the research of rough set theory. The reduction algorithm based on the rough set theory can be used in the areas of knowledge acquisition, machine learning, and so on. However, knowledge reduction is dependent on a series of supporting algorithms such as the calculation of attribute significance,finding core,attribute reduction. So, desigh an implementation of reduction algorithm is one of important contents of rough set research.Besides, there are mistakes or missing data in the data set due to various reasons, so, that how to reduce attributes on incomplete information system is a new study aspect.In this thesis, we conduct some research on Rough Set models in both complete and incomplete information system. My main results are as follows:1,We study the basic knowledge of rough set, introduce the interrelated definitions and propertise of the approximation relation.2,Under the incomplete information system, based on researching and analyzing the reduction algorithms in Rough Set, we bring up a new definition of differentiability matrix based on the discernibility matrix, then gives an algorithm.3,Started with the equivalence relations of classical rough set model, we put forward five kinds of extensions rough set model. Not only does the extension reserve the advantage of the present model, but also these models discard the disadvantage of the present model. 4,To go a step further, we spread the definitions and properties of the complete information system with the equivalence relations to the incomplete information system with the tolerance relations. In the meanwhile, we also spread the differentiability matrix and discernibility matrix to it. At last, we give a reduction algorithm based on the judgements theorem of attributes reduction.