The Application Of Data Mining Technology In The University Library Management

The development of library automation and digitization, the library has massive data information, at the same time, the library also faces the problem of how to use this information to discover useful knowledge to improve the quality of library services. Based on the above issues, we have an idea that is the application of data mining technology in library management.When we apply the data mining technology to the library daily routine, we can find out some valuable rules which are contained in the massive data information of library. Take advantage of these rules, we can make the management of library stacks more optimization, we can guide the borrow behavior of readers, we can develop the individualization services, at the same time, these rules also provide reference for the information services of library and the collecting of information resources and the scientific arrangement of library works.The paper summarizes the research significance and importance of data mining technology, and basic concept, process, function, common techniques as well. Decision tree method and association rules analysis are used. By comparing several common association rules algorithms, the classical algorithm-Apriori is researched deeply. And then, the paper analyzes the necessity and feasibility of applying data mining technology to college library management. After that, the design thought and functional objective of intelligent recommendation system for college library based on decision tree method and association rules analysis is proposed.The paper describes the design scheme of intelligent recommendation system for college library in detail, including requirements analysis, database design, system design, function module design, the implement of decision tree module and association rule module. Finally, the shortage of this system and the further research direction are indicated.This paper proposes an improved approach about Apriori algorithm applying to library. Comparing with original Apriori algorithm, the improved Apriori algorithm has better superiority in effect and efficiency of data mining, and provides guidance to readers’borrow behavior. Similarly, the proposed approach can provide reference value in arranging shelf location and optimizing stack management.