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.
|Number of pages||17|
|Journal||Journal of Library and Information Studies|
|State||Published - 2021|
Bibliographical noteFunding Information:
This research was partly supported by JST ACT-X grant number JPMJAX1909.
© 2021, National Taiwan University, Department of Library and Information Science. All rights reserved.
- Bibliometric Analysis
- Computational Methods
- Library and Information Science
- Text Mining
- Vocabulary Construction
ASJC Scopus subject areas
- Library and Information Sciences