Application Of Association Rules Mining In Data Processing Of Borrowed Books

As an important knowledge base for teachers and students, university libraries cover a very wide field of books. Books are bought annually, so the number of books increases constantly. It is not pleasant for teachers and students to find books relevant to their own needs, so optimizing the layout of books quickly and effectively becomes more and more important for teachers and students. The dissertation is to mine the association information among the borrowed books from the history data quickly and effectively with association rules methods of data mining.Firstly, the theoretical foundation of data mining is described, some classic association rules algorithms of data mining are analyzed and summarized. Then, the history data of borrowed books is preprocessed according to the algorithm, including data input and extraction, the establishment of transaction database. At last, a new association rules algorithm MFP-Miner is applied to the transaction database, association rules of the borrowed books are mined. Experimental results showed that the algorithm is superior to other algorithms in efficiency.Due to the data of borrowed books is updated daily, the database becomes bigger continuously, and as the need of association degree is different, the minimal support is changed sometimes. In order to meet the need of teachers and students to borrow books as convenient as possible, it is necessary to update the mined results constantly. If previous mining algorithms (such as MFP-Miner) are used to mine the whole database for the second time, it will waste a lot of time,and the information that has been mined before will be abandoned. So the MFIA-IU algorithm is proposed to mine comprehensive updated maximum frequent sets when the database and the minimal support change simultaneously. Thereby, the annually repeated mining of the old data is avoided.