Researched For Attribute Reduction Algorithm Based On Rough Set Theory

Rough set theory is provided by Z.Pawlak in 1982.It is a math theory that process the non-accurate after probability theory, fuzzy theory and Dempster-Shafer. Not needing other information or previous knowledge this theory can analyze and process the non-accurate, non-integrity data and then mine latent knowledge.Data mining and knowledge discovery in databases is drawing knowledge from the database, data warehouse or other databases.Rough set theory is a new data mining technology.The attribute reduction is a key technology in rough sets for data mining. In this paper, I have studied the rough set theory and made a distinction based on the matrix and attribute importance of improving the algorithm. In the decision-making table for the incompatibility problem, I made a new attribute reduction algorithm based on the U / ind (C) equivalence class of the reduction algorithm Finally, in summing up the characteristics of each of these algorithms, this paper made a new algorithm based on a tree by the reduction algorithm. The algorithm is characterized by the decision table can be all for compatible and incompatible decision table.