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With the continuous enrollment of colleges and universities in recent years , the number of students in schools , and the number of teachers increased significantly , has posed a serious test for the management and teaching college students , the traditional teaching management tools have been gradually adapt to social development . Colleges and universities have a lot of information systems and various types of databases , such as school management system, performance management system, personnel management systems, etc. These systems and databases have accumulated large amounts of data , but only by the lack of the necessary information technology and means of management personnel simple statistical analysis , sorting , backup and other functions for surface information hidden in the data behind the information can not be effectively utilized . Data mining is the implicit mode was found from the historical data set , and apply these patterns to predict . Data mining technology to analyze large amounts of data already on the basis of scientific research, business decisions or business management, so as to achieve the purpose of decision support services . Association rule mining comparison is one of the most active research directions in the field of data mining , and it reflects an event , and other events are directly dependent or associated knowledge . Firstly a general discussion of the data mining , including the history , the concept of data mining , and related technologies . Then , data mining association rules mining algorithms to do in-depth research and analysis of the association rule mining algorithm in the classic the Apriori algorithm AprioriTid algorithm , summed up the problem exists in the algorithm , then in AprioriTid algorithm based on the The improved algorithm . Finally, the improved algorithm , based on the standard data mining process mining computer program design basis VF \provide a basis to improve the quality of teaching . Universities can tap the information is not just the results, you can also age students ( thinking cognitive maturity ) , gender, hobbies, family background , health status , student status , education, college entrance examination scores , course content, examination papers , teachers, etc. data mining , which provide basis for decision making for managers and teachers , individualized education, raise the level of university teaching and teaching management effectiveness .
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