Analysis of students’ borrowing behavior in local university library using data mining technique

Abstract

This paper analyzes the reader's borrowing behavior data using data mining technique specifically K-Means algorithm and finds the hidden user borrowing characteristics and demand preference information. The dataset used in the study is based on the borrowing behavior data of readers in the library automatic management system of the university. Moreover, this paper discusses the use of K-means cluster analysis method in data mining technology to analyze and mine them and finds the readers' reading tendency and personal interest information, in order to provide reference for the development of personalized active service and the optimal allocation of collection resources.