Rough Set Knowledge Reduction Discovery And Appliction

Rough set theory was proposed firstly by Z Pawlak who is the scientist in Poland in 1982. It is a math theory which process the non-accurate after probability theory and fuzzy theory. It is based on the indiscernibility relation that describes indistinguishable objects, and concepts are represented by lower and upper approximations. Not needing other information this theory can analyze and process the non-accurate, non-integrity and incomplete data. Rough set theory is used in many fields such as data mining, machine learning, pattern identifying and decision-making.The attribute reduction of Information system is the main topic in rough set theory. Getting the best reduction or all reduction is the NP problem. Heuristic algorithm based on the attribute importance had been made to get better reduction.In the process of attribute reduction, attribute reduction algorithms based on discernibility matrix is proposed. Because of its high time complexity, its improved algorithm which is based on the frequency of attribute is proposed. MIBARK algorithm defined the attribute importance using the information entropy. The effectiveness of the three algorithms in...