Clustering-Based Approach for Clustering Journals in Computer Science

J. K. D. B. G. Jayaneththi and Banage T. G. S. Kumara
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J. K. D. B. G. Jayaneththi: Sabaragamuwa University of Sri Lanka, Sri Lanka
Banage T. G. S. Kumara: Sabaragamuwa University of Sri Lanka, Sri Lanka

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2019, vol. 9, issue 2, 35-51

Abstract: In the present scientific world, most of the authors of scientific literature are seeking effective ways to share their research findings with large peer groups. But finding a high-quality journal to publish paper is a huge challenge. Most of the journals present today are predatory and less-quality. The main aim of this study is to help the researchers in identifying the quality level of computer science journals by introducing a data mining model based on six journal quality metrics (Journal Impact Factor, SCImago Journal Rank, Eigenfactor, H-index, Source Normalized Impact per Paper, and Article Influence). Further, another objective is to identify the best metrics to measure the quality of journals out of the six attributes. A sample dataset of 200 journals was used and journals were clustered into five clusters using K-means clustering algorithm. When finding the best quality metrics, Pearson's and Spearman's correlation coefficients were calculated. A more accurate clustering model with an accuracy of 0.9171 was developed considering only suitable attributes.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jssoe0:v:9:y:2019:i:2:p:35-51

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