基于购物篮理论的图书借阅数据挖掘  

Book Lending Data Mining Based on Shopping Basket Theory

作　　者：吕俊杰 Lyu Junjie(Southwest University of Political Science and Law Library, Chongqing 401120)

机构地区：[1]西南政法大学图书馆,重庆401120

出　　处：《情报探索》2019年第5期79-84,共6页Information Research

摘　　要：[目的/意义]旨在为优化图书采购,提高纸质图书流通效率提供参考。[方法/过程]借鉴超市购物篮分析理论,利用描述分析、Apriori关联规则等数据挖掘方法,对西南政法大学图书馆近8年的借阅数据进行多维度展示。[结果/结论]图书馆在采购图书方面应倾向于院系师生对图书的需求采集,同时根据借阅情况对采购复本进行有选择的控制,降低图书采购成本;改变传统的排架方式,针对强关联图书和热门图书进行专门归置,以期进一步提高借阅量。[Purpose/significance] The paper is to provide references for optimizing book purchasing and improving circulation efficiency of printed books.[Method/process] The paper draws on the theory of shopping basket, and uses data mining methods such as descriptive analysis and Apriori association rules to analyze the lending data of Southwest University of Political Science and Law Library in recent eight years from multiple perspectives.[Result/conclusion] The library should be inclined to collecting the faculty and students’ demands for books in book purchasing, selectively control the duplicate books acquisition according to the lending situation to reduce the purchase cost;change traditional ways of shelving and exclusively place the strongly associated books and popular books together, so as to further improve the amount of borrowing.

关 键 词：购物篮分析 图书馆 借阅行为 数据挖掘 APRIORI 

分 类 号：G250[文化科学—图书馆学]