Knowledge Acquisition Research Based On Rough Set In Incomplete Information System

Human's knowledge is continuously enriched and updated. However, by contrast to the objective world, it is incomplete, undependable and uncertain. Human beings have been continuously and gradually learning the objective world by using the imprecise and incomplete knowledge. Rough set theory is a mathematical tool used for dealing with vagueness and uncertainty. Simultaneously, the classical rough set theory is based on complete information. In this paper, Knowledge Discovery based on rough set theory under incomplete information is researched.In the paper, an overview of the current situation of researches on Rough Set, and the main issues related to the incomplete data problem and the commonly-used methods of handling incomplete data problems are detailed. Then rough set theory's basic concepts and properties are summarized. Binary relation and attribute reduction algorithms of incomplete information system are also summarized. Based on the theory, probability discernibility matrix is defined and corresponding discernibility function is given. Then a probability algorism for attributes reduction is proposed. The probability that the attribute belong to the reduction can be know from the reduction which is gained by the algorism, and we can sample according to this reduction.Along with the rapid increase of data, the incremental data mining has raised wide concerns. In this paper, attribute price (P) is defined and a new incremental algorithm for attribute reduction is provided. The attribute reduction of new information system can be got by the algorithm in the dynamic mode when a condition attribute is added to the information system without the change of object and decision attribute. At last, the dynamic reduction algorithm is improved and the improved algorithm is given.