Version 1
: Received: 19 June 2024 / Approved: 19 June 2024 / Online: 20 June 2024 (15:37:47 CEST)
How to cite:
Chigarev, B. Keyword Co-Occurrence Analysis Using the FPGrowth Algorithm. An Example of Energies Journal Bibliometric Data for 2023-2024. Preprints2024, 2024061380. https://doi.org/10.20944/preprints202406.1380.v1
Chigarev, B. Keyword Co-Occurrence Analysis Using the FPGrowth Algorithm. An Example of Energies Journal Bibliometric Data for 2023-2024. Preprints 2024, 2024061380. https://doi.org/10.20944/preprints202406.1380.v1
Chigarev, B. Keyword Co-Occurrence Analysis Using the FPGrowth Algorithm. An Example of Energies Journal Bibliometric Data for 2023-2024. Preprints2024, 2024061380. https://doi.org/10.20944/preprints202406.1380.v1
APA Style
Chigarev, B. (2024). Keyword Co-Occurrence Analysis Using the FPGrowth Algorithm. An Example of Energies Journal Bibliometric Data for 2023-2024. Preprints. https://doi.org/10.20944/preprints202406.1380.v1
Chicago/Turabian Style
Chigarev, B. 2024 "Keyword Co-Occurrence Analysis Using the FPGrowth Algorithm. An Example of Energies Journal Bibliometric Data for 2023-2024" Preprints. https://doi.org/10.20944/preprints202406.1380.v1
Abstract
Background. Keyword co-occurrence analysis is a crucial tool for comprehending research trends, identifying relevant studies, and gaining insight into the connections between various concepts and topics. Objective. This study focuses on analyzing the co-occurrence of keywords using FP-growth algorithm and direct search methods. Materials and methods. The methodology involved extracting bibliometric data of Energies journal for 2023-2024 from MDPI publisher platform, keyword lemmatization and keyword co-occurrence estimation. Clustering and visualization were performed using Multidendrograms and Scimago Graphica software. Results. The results showed that the FP-growth algorithm can achieve a close match with the direct search results, which facilitates data preparation for clustering. In addition, finding the co-occurrence of three or more keywords significantly reduced the number of possible combinations, which allowed the identification of specific research topics. Conclusions. This study highlights the usefulness of the FP-growth algorithm in keyword analysis and provides insights into ways to refine search queries to abstract databases for the purpose of designing and writing literature and systematic reviews.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
