The Research & Application Of Data Mining Model Based On Rough Set Theory

Data mining is the process of discovering interesting knowledge from large amounts of Data stored either in Databases , Data Warehouses , or other information repositories.Rough Set (RS) theory was put forward by Pawlak Zdzislaw in 1982. After about 20 yesrs of developing, it has received fruitful achievements in both of theory and applications. RS doesn' t depend on additional information beyond the data set, which is a potent tool for dealing with vague, imprecise,incomplete and uncertain data, and is a new technology in Data Mining.Some trasitional method of data mining is only suitable for precise set, not for rough set. Since many set of data in real life is rough, the model of data Mining baesd on Rough Set Theory Plays an important role in information system.In the Dissertation, A Data Mining Model Based on Rough Set Theory is Brought forward. Then two Attribute-reducing Algorithms:discernibility matrix algorithm and Greedy Rough Set Reducing algorithm are put forward. Meanwhile the rule Extractioon algorithm and a discretization method for continuous attributes are put forth too.A Practical system is successfully constructed based on the Data Mining Model Presented in the Dissertation. The rules extracted by the RS Algorithm are in accord with the knowlwdge of erpert. All these proved that the model is advanced and practical.