Analysis Of Financial Data Based On Rough Set Theory

With the development of technique of database, quantity of data in database increases rapidly. When data is stored into database continuously, people more and more show interesting in the association among item sets in database, which is called association rule. Nowadays, association rule has been applied to all kinds of fields, such as medical treatment, market analysis. However, the efficiency of traditional algorithms of association rule mining is lower, and people may show interesting in only some of association rules mined. Thus, It is important to mine association rules effectively which people are interesting in.The research of this thesis is mainly about the dynamic reduction based on rough set theory as well as how to establish and develop the decision rules after completing the reduction. With the basic principle of rough set, customers dig out the decision rules they wanted.We focus on following several aspects:(l)An improved attributes reduction algorithm;(2) Connecting the improved attributes reduction algorithm with other algorithms.Rough set theory, which is a powerful tool in dealing with vagueness and uncertainty,forms an appealing foundation for data mining. Based on the research of rough settheory, we discussed the arithmetic of attributes reduction. Then we discuss animproved attributes reduction algorithm in detail and use the algorithm in a real caseof bank.. The result shows the effectiveness of the improved algorithm..