The Original Supporting System Of Estimating Teachable Quality

Data warehouse and data mining are the most active branches of database studying,developing and application, and also they are the key factors of DSS. Data warehouse is a decision supporting,subject-oriented,integrated,stable and time-dependent collection of data.Data mining is to analyze data and acquire knowledge from database and warehouse using the method of artificial intelligence.The binding of them will provide a strong basis of decision analyzing for enterprises and related departments.Most of the previous teaching systems are that have no ability of synthetic analysis, decision support,and the utilization of hidden knowledge from vast history information.Analysis of teaching management is an important way to teach evaluation. It is necessary to guarantee the quality of teaching and improve the stuff of students analyzing the data made in processes of tests and teaching with the results of analysis.The thesis introduces the principles of Data Mining and the methods of Association Rule in detail. Stem from these theory,the paper analyses the classic Association Rule algorithm-Apriori from theoretical and practical perspective.To make up for its deficiency,a new algorithm has been proposed,which utilizes Candidates C_k to shrink the number of records in database.The author presents intensively the process of designing the Education Management System,especially the module of Teacher Testing.Applied to this module,the Adapted Apriori Algorithm mines a multitude of association rules for manager decision.