Application of Data Mining in Library-Based Personalized Learning

Lin Luo


this paper expounds to mine up data with the DBSCAN algorithm in order to help teachers and students find which books they expect in the sea of library. In the first place, the model that DBSCAN algorithm applies in library data miner is proposed, followed by the DBSCAN algorithm improved on demands. In the end, an experiment is cited herein to validate this algorithm. The results show that the book price and the inventory level in the library produce a less impact on the resultant aggregation than the classification of books and the frequency of book borrowings. Library procurers should therefore purchase and subscribe data based on the results from cluster analysis thereby to improve hierarchies and structure distribution of library resources, forging on the library resources to be more scientific and reasonable, while it is also conducive to arousing readers' borrowing interest.


Digital Library; Clustering Algorithm; DBSCAN algorithm

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Copyright (c) 2017 Lin Luo

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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