Application Of Improved Apriori Algorithm In Teaching Evaluation

With the development and popularization of computer network technology, the data acquisition, management, query and other technology have developed rapidly. Living in the tsunami surge of data "ocean", people need some other intelligent technology urgently, so that they can explode a more valuable oilfield from the ocean,. In response to the need, the data mining technique has emerged, which has been applicated in business, medical, financial and other fields successfully. The main technology of data mining included classification analysis, clustering analysis, association rules analysis etc. The technology of association rules analysis is an important branch of data mining technique. It can extract the value relations or rules that people want to know from the data warehouse.Colleges have an active impact on evaluating teachers’ teaching, encouraging teacher’s teaching enthusiasm, enhancing the teaching level, improving the management methods, and promoting the comprehensive strength. Therefore, it is necessary for colleges to combine the data mining technique with the evaluation of teaching, and make full use of it to analyze the evaluation data and to get some useful imformation that can help the college’s governors to make a better management and improve the teaching quality.The thesis made a brief introduction of teaching evaluation, analyzed the problems that commonly appeared in teaching evaluation, and explained the significance of combining association rules technology with teaching evaluation. Besides, it also expounded the related concept of association rules analysis, introduced the Apriori algorithm in great detail, and implemented the compression algorithm of AMC based on matrix because of the expensive Apriori algorithm I / O, large candidate generation and not suitable for multivalued attribute database. AMC algorithm made an improvement of the Apriori algorithm from many perspectives, which reduced the size of the database, the times of scanning database and unnecessary candidate set generation, but also omit the pruning operation and terminate the algorithm in advance, so that the efficiency of the algorithm has great improvement in both space and time.In the end, the thesis has dug out some useful rules to our school teaching evaluation data with the AMC algorithm, and made an analysis of the results, came up with the solutions to improve the quality of teaching, and provided decisionmaking and helps for the teaching administrators.