Adoption of data mining methods in the discipline of library and information science

Marie Katsurai, Soohyung Joo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalJournal of Library and Information Studies
Volume19
Issue number1
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
This research was partly supported by JST ACT-X grant number JPMJAX1909.

Publisher Copyright:
© 2021, National Taiwan University, Department of Library and Information Science. All rights reserved.

Keywords

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

ASJC Scopus subject areas

  • Library and Information Sciences

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