The Research Of Data Mining Method Based On Rough Set Theory

With the rapid development of database technology and the abroad application of Database Management System, So data is excessive but knowledge is spare. It is certain that we have to deal with massive data and abstract implicit regularity from massive data. Under this condition, Data Mining as the tool of dealing with the abundant data comes into being. Rough Set Theory is a kind of valid method to deal with the complicated systems. Rough Set Theory can effectively analyze and deal with kinds of incomplete information, and find implicit information from it.This thesis illuminates the theory about Data Mining and Rough Set. On this basis,the thesis conducts in-depth analysis of Data Mining Process based on Rough Set,then studies and analyses the algorithms used in these processes. What the paper does basically is in two points as follows:1. Attribute reduction algorithms of Rough set theory are studied. Attribute reduction algorithm based on rough set theory is a critical step in data mining model. By comparing different attribute reduction algorithm,and For the shortage of different algorithms, the article proposes an attribute reduction algorithm based on reduction tree attribute. The method reduces the spending of the forming and storing discernibility matrix, and simplifies process of all reduction in decision-making system.2. Attribute value reduction algorithms of Rough set theory and application of data mining based on rough sets in the field of telecommunications are studied. This section describes the basic algorithm of the attribute value reduction. For lack of basic algorithms, an improved attribute reduction algorithm is proposed and the improved algorithm of the attribute reduction is analyzed. This section also provides its application in the field of communications, experimental analysis and conclusions.