Knowledge Discovery And Factor Reduction Based On Factor Space Theory

In this era of information explosion, information and data in various fields have increased dramatically. Database-based knowledge discovery and knowledge reduction are important ways to extract useful information from massive data. Factor space theory as an important mathematical theory of intelligent information processing, it put forward the mathematical framework of knowledge representation. In the first part of this paper, the knowledge discovery method based on factor analysis table is given, and compared with the existing equivalence relation method based on information system. The second part defines the operation of the factor and defines the factor space generated by the factor analysis table. The third part gives the knowledge reduction method based on the factor space and uses the information entropy to associate the knowledge and the information together. The information entropy is used to give the determinant of the factor reduction.