Data Mining Methods Research Based On Rough Sets Theory And Support Vector Machines

This thesis studies some key technology questions in data mining based on rough set theory firstly. It is well known that there are usually much redundant data in large knowledge repository. These data waste the storage space and disturb making decision. In the thesis, the knowledge reduction is studied from equivalent relation of attribute system angle of view, from the dependent level of attribute and importance of attribute angle of view, from the discernibility matrix angle of view, from the viewpoint of information theory. By experimental studies, five ways reduced the attributes were obtained. Meanwhile we discover that the changing tendency of the information entropy is non-rigorous monotonically decreasing in comentropy, when the number of the attributes is increasing.In fact, the sorted boundary is very accurate when it is classified on the basis of classical rough sets theory. Although accuracy improves greatly in those ways for the classification recognition, its fault tolerance and the serviceability of model is very poor. For removing the defects, this thesis has studied variable precision rough sets theory and its reduction.Then it studied modeling principle of classification method of SVM and regression analysis of SVM, and studied their sphere of application and problem solving. At one time, it was discovered that its merits is hidden danger in data mining. If there are noise or contradictory information, results prediction based on small sample set will be greatly influenced. Before forecasting and classifying of SVM, these questions have been found and foreclosed, which is precisely what rough sets theory has advantage.Upon that, based on advantage and virtue of the rough sets theory and SVM methods, this paper studied how to combine two methods and obtained procedure methods which organically combine RST(Rough Sets Theory) with more than one classification learning machine of SVM. Also, it has given a method that through them construct more than one classification learning machine, and described it with examples.