基于数据挖掘的图书馆读者借阅行为分析

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英文篇名：Analysis of data mining based borrowing behavior of library readers 作者：崔金环 ; 解海 英文作者：CUI Jinhuan;XIE Hai;Xi'an Innovation College of Yan'an University;Xi'an Space Expedition Fluid Control Limited Company; 关键词：数据挖掘 ; 图书馆读者 ; 借阅行为 ; Jaccard相似系数 ; 对称矩阵 ; 喜好指数 英文关键词：data mining;;library reader;;borrowing behavior;;Jaccard similarity coefficient;;symmetric matrix;;preference index 中文刊名：XDDJ 英文刊名：Modern Electronics Technique 机构：延安大学西安创新学院;西安航天远征流体控制股份有限公司; 出版日期：2019-01-01 出版单位：现代电子技术 年：2019 期：v.42;No.528 语种：中文; 页：XDDJ201901039 页数：5 CN：01 ISSN：61-1224/TN 分类号：174-178

摘要

针对传统方法存在对图书馆读者借阅行为数据利用率低、对读者图书借阅行为分析不准确的问题,提出基于数据挖掘的图书馆读者借阅行为分析方法。采用基于相似系数矩阵的聚类算法,对图书馆读者借阅行为实施分析,采用Jaccard相似系数度量高维度图书馆读者借阅数据的相似度,对高维度读者借阅数据进行聚类分析,解决图书馆读者借阅数据维度高的问题。构建聚类算法时塑造了新矩阵,当新矩阵中的所有元素都大于初始阈值时,说明数据聚类过程结束,聚类算法的构建实现图书馆读者借阅行为数据的有效分类,针对读者设计个性化专属图书推荐服务。分析了所提方法的应用过程,对图书馆读者借阅图书信息数据实施预处理后,进行读者借阅行为分析。实验结果说明,所提方法能提高图书馆读者借阅行为数据的利用率,具有较高的执行效率和CPU利用率,对读者图书借阅行为分析能力强。
        The traditional hybrid attribute method based on rough set has the problems of low utilization rate of library readers′ borrowing behavior and inaccurate analysis of readers′ book borrowing behavior. Therefore,the data mining based behavior analysis method of library readers is proposed. The clustering algorithm based on similarity coefficient matrix is used to analyze the borrowing behavior of library readers. Jaccard similarity coefficient is used to measure the similarity of high-dimensional borrowing data of library readers to reduce the dimensions of borrowing data of library readers. The new matrix is constructed while building the clustering algorithm. If all the elements in the new matrix are greater than the initial threshold,the data clustering process is completed. The construction of clustering algorithm can realize the effective classification of library readers′ behavior data,and design the recommendation service of personalized exclusive books for readers. The practical application process of the proposed method is analyzed, and the book borrowing information data of library readers is preprocessed to analyze the readers′ borrowing behavior. The experimental results show that the proposed method can improve the utilization rate of library readers′ borrowing behavior data,and has high execution efficiency and CPU utilization rate,and strong ability of book borrowing analysis behavior of readers.

引文

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