The Research And Application Of Data Mining Technology On The Attribute Reduction Of Rough Set Theory

Data mining attracts great attention in information industry. The major reason is that large amount of existing data may be used widely, and it is urgently necessary to convert these data into useful information and knowledge.There are some methods for data mining, Rough Sets methodology is one of important methods, applying rough theory in data mining field can improve the analyzing and learning ability for incomplete data of large database, which has extensive applied prospect and applied value.Attribute reduction is a significant topic of rough set theory, it could improve potential knowledge definition of system, lower time complexity of discovering the rules, and raise discovering efficiency if the redundant attributes could be eliminated.This paper researches a data mining technology based on attributes reduction of rough set theory. Firstly, rough set theory has been discussed, by the analysis and synthesis of data mining algorithm based on rough set theory, an improved heuristics algorithm of attributes reduction was proposed, which was based on the weighed sum and frequency algorithm of Bi-directional selection attribute reduction. Secondly, it introduced data mining system, one is rules generation, it creates rules after attributes are reduced by the attributes reduction algorithms above; the ther is classification, it gives a classification for new objects according to these rules which have been created in rules generation. Finally, the experimental results show that the algorithm is verified to be more feasible and effective.