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Clustering-Based Approach for Clustering Journals in Computer Science

Clustering-Based Approach for Clustering Journals in Computer Science

J. K. D. B. G. Jayaneththi, Banage T. G. S. Kumara
Copyright: © 2019 |Volume: 9 |Issue: 2 |Pages: 17
ISSN: 1947-3052|EISSN: 1947-3060|EISBN13: 9781522566557|DOI: 10.4018/IJSSOE.2019040103
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MLA

Jayaneththi, J. K. D. B. G., and Banage T. G. S. Kumara. "Clustering-Based Approach for Clustering Journals in Computer Science." IJSSOE vol.9, no.2 2019: pp.35-51. http://doi.org/10.4018/IJSSOE.2019040103

APA

Jayaneththi, J. K. & Kumara, B. T. (2019). Clustering-Based Approach for Clustering Journals in Computer Science. International Journal of Systems and Service-Oriented Engineering (IJSSOE), 9(2), 35-51. http://doi.org/10.4018/IJSSOE.2019040103

Chicago

Jayaneththi, J. K. D. B. G., and Banage T. G. S. Kumara. "Clustering-Based Approach for Clustering Journals in Computer Science," International Journal of Systems and Service-Oriented Engineering (IJSSOE) 9, no.2: 35-51. http://doi.org/10.4018/IJSSOE.2019040103

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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.

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