Dataset Information

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.

ABSTRACT:

Background

There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The study aimed to put forth a clustering algorithm for cluster analysis (called following leader clustering [FLCA], a follower-leading clustering algorithm), examine the dissimilarities in cluster outcomes when considering collaborations with and without self-connections in cluster analysis, and demonstrate the application of the clustering algorithm in bibliometrics.

Methods

The study involved a search for articles and review articles published in JMIR Medical Informatics between 2016 and 2022, conducted using the Web of Science core collections. To identify author collaborations (ACs) and themes over the past 7 years, the study utilized the FLCA algorithm. With the 3 objectives of; Comparing the results obtained from scenarios with and without self-connections; Applying the FLCA algorithm in ACs and themes, and; Reporting the findings using traditional bibliometric approaches based on counts and citations, and all plots were created using R.

Results

The study found a significant difference in cluster outcomes between the 2 scenarios with and without self-connections, with a 53.8% overlap (14 out of the top 20 countries in ACs). The top clusters were led by Yonsei University in South Korea, Grang Luo from the US, and model in institutes, authors, and themes over the past 7 years. The top entities with the most publications in JMIR Medical Informatics were the United States, Yonsei University in South Korea, Medical School, and Grang Luo from the US.

Conclusion

The FLCA algorithm proposed in this study offers researchers a comprehensive approach to exploring and comprehending the complex connections among authors or keywords. The study suggests that future research on ACs with cluster analysis should employ FLCA and R visualizations.

SUBMITTER: Cheng TY

PROVIDER: S-EPMC10589539 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.

Cheng Teng-Yun TY Ho Sam Yu-Chieh SY Chien Tsair-Wei TW Chow Julie Chi JC Chou Willy W

Medicine 20231001 42

<h4>Background</h4>There are 3 issues in bibliometrics that need to be addressed: The lack of a clear definition for author collaborations in cluster analysis that takes into account collaborations with and without self-connections; The need to develop a simple yet effective clustering algorithm for use in coword analysis, and; The inadequacy of general bibliometrics in regard to comparing research achievements and identifying articles that are worth reading and recommended for readers. The stud ...[more]

PMID: 37861508

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Project description:BackgroundA new approach to showcasing author publications on a website involves using a visual representation instead of the conventional paper list. The creation of an impact beam plot (IBP) as a research profile for individuals is crucial, especially when incorporating collection edges that include self-cited articles through a rare cluster analysis technique not commonly found in the literature. This study presents the application of a unique method called the following-leading clustering algorithm (FLCA) to generate IBPs for 3 highly productive authors.MethodsFor the 3 highly productive authors, Sung-Ho Jang from South Korea, Chia-Hung Kao from Taiwan, and Chin-Hsiao Tseng from Taiwan, all their published articles indexed in the Web of Science Core Collection were downloaded. Sung-Ho Jang published 593 articles, Chia-Hung Kao published 732 articles, and Chin-Hsiao Tseng published 160 articles. To analyze and showcase their publications, the FLCA was utilized. This algorithm helped cluster their articles and identify representative publications for each author. To assess the effectiveness and validity of the FLCA algorithm, both network charts and heatmaps with dendrograms were employed. IBPs were then created and compared for each of the 3 authors, taking into consideration their h-index, x-index, and self-citation rate. This allowed for a comprehensive visual representation of their research impact and citation patterns.ResultsThe results show that these authors' h-index, x-index, and self-citation rates were (37, 44.01, 1.66%), (42, 61.47, 0.23%), and (37, 40.3, 6.62%), respectively. A higher value in these metrics indicates a more remarkable research achievement. A higher self-citation rate with a lower cluster number indicates that manuscripts are more likely to have been self-drafted. Using the FLCA algorithm, IBPs were successfully generated for each author.ConclusionThe FLCA algorithm allows for the easy generation of visual IBPs based on authors' publication profiles. These IBPs incorporate 3 important bibliometric metrics: h-index, x-index, and self-citations. These metrics are highly recommended for use by researchers globally, particularly with the self-citation rate, as they offer valuable insights into the scholarly impact and citation patterns of individual researchers.

Project description:Recommender systems have become a core component of various online platforms, helping users get relevant information from the abundant digital data. Traditional RSs often generate static recommendations, which may not adapt well to changing user preferences. To address this problem, we propose a novel reinforcement learning (RL) recommendation algorithm that can give personalized recommendations by adapting to changing user preferences. However, a significant drawback of RL-based recommendation systems is that they are computationally expensive. Moreover, these systems often fail to extract local patterns residing within dataset which may result in generation of low quality recommendations. The proposed work utilizes biclustering technique to create an efficient environment for RL agents, thus, reducing computation cost and enabling the generation of dynamic recommendations. Additionally, biclustering is used to find locally associated patterns in the dataset, which further improves the efficiency of the RL agent's learning process. The proposed work experiments eight state-of-the-art biclustering algorithms to identify the appropriate biclustering algorithm for the given recommendation task. This innovative integration of biclustering and reinforcement learning addresses key gaps in existing literature. Moreover, we introduced a novel strategy to predict item ratings within the RL framework. The validity of the proposed algorithm is evaluated on three datasets of movies domain, namely, ML100K, ML-latest-small and FilmTrust. These diverse datasets were chosen to ensure reliable examination across various scenarios. As per the dynamic nature of RL, some specific evaluation metrics like personalization, diversity, intra-list similarity and novelty are used to measure the diversity of recommendations. This investigation is motivated by the need for recommender systems that can dynamically adjust to changes in customer preferences. Results show that our proposed algorithm showed promising results when compared with existing state-of-the-art recommendation techniques.

Dataset Information

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.

Background

Methods

Results

Conclusion

Publications

A comprehensive approach for clustering analysis using follower-leading clustering algorithm (FLCA): Bibliometric analysis.

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