Presentation of Relative Partition Granularity of Attributes Reduction for Decision Table

Knowledge and classifications are related together by the theory of rough sets which claim is that knowledge is deep-seated in the classificatory abilities of human beings. In this paper, we firstly give a quantitative representation of the ability of knowledge's classification, and provide a novel representation for knowledge, that is, it can be expressed by partition granularity. Secondly, the relative partition granularity is defined, and its qualities are discussed, where the relative partition granularity can be used to descript the classification ability of conditional attributes relate to decision attribute. Finally, the equivalence between the algebraic representation and the relative partition granularity representation is proved for a consistent decision table.