Association Rules And Gender Analysis In Library Management

As one of the main source of Knowledge, University library has become an important place for students to learn skills to solve the problems and equip themselves. However, with the explosive growth of the library collection, how to find the most suitable book for their own is becoming increasingly difficult. To serve the students better, help them find what they really need efficiently, libraries must have a good understanding of their own collections as well as students borrow habits, adjust the procurement and start building a book recommendation system with higher accuracy. In order to solve the above problem, this article propose an idea to this porblem, namely using association rule mining to analyze the library’s transaction data multi-dimensionally, help the library manage the procurement and books recommended services with the conclusions.This paper describes the theoretical foundation of data mining, the domestic and international profile of library users analysis as well as the domestic and international profile of gender analysis. Then based on the need for guide procurement decisions and book recommendation system, apply an analysis to the specific borrow data.The first part of the paper’s body analyzes the association rules between the books category, that analysis of what kind of books often been borrowed together. Specific analysis levels includes:mining all students;mining disaggregated by sex for all students;mining by profession for all students;mining disaggregated by sex for each profession. Through the analysis and comparative analysis of each level, we can get rules like what kind of books all the students often borrow,what kind of books specific major often borrow and etc. Such rules can be used to guide purchasing decisions. Other rules that show the relationship between majors and books can be used to establish the books recommended system.The second part analyzes the association rules between the books and other attributes, the conclusions can guide libraries to build more accurate, more targeted book recommendation system.This part includes the analysis between book borrowing cycle and books category and the analysis between academic performance of students and books to borrow. The second part of analysis first classified the data by grade, then apply association mining on each grade, which can reveal the trend changes over different grades. These two factors of borrowing cycle and academic performance of students can help the libraries screen high-quality and more suitable books to push.By analysis of the two parts of association rules, the libraries can get valuable information to guide their purchasing decisions and book recommended services.