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Adoption of Data Mining Methods in the Discipline of Library and Information Science

Volume / Date

 VOL. 19 (NO. 1) / JUN.2021

Page

 pp. 1-17

DOI

 10.6182/jlis.202106_19(1).001

Language

 English

Marie Katsurai, Soohyung Joo*

* Corresponding Author

Marie Katsurai Department of Intelligent Information Engineering and Sciences, Doshisha University, Kyoto, Japan
ORCID: 0000-0003-4899-2427
Soohyung Joo* School of Information Science, University of Kentucky, Lexington, Kentucky, USA
ORCID: 0000-0001-6770-5118
soohyung.joo@uky.edu

Abstract

The purpose of this paper is to explore the recent trends of data mining method adoption in the library and information science (LIS) discipline. Bibliographic records from the data mining and LIS fields were collected respectively from the Scopus database. A dictionary of data mining method terms was constructed based on a rule-based textual analysis. Using the dictionary, this study investigated a range of prevalent data mining methods utilized in recent LIS studies. The findings of this study reveal different areas of data mining methods employed in LIS, such as big data, machine learning, text mining, information retrieval, and dimension reduction. The study also confirms the recent popularity of machine learning techniques in LIS research.

Keywords

Library and Information Science; Text Mining; Vocabulary Construction; Bibliometric Analysis; Computational Methods