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Clustering Scientometrics of Computer Science Journals for Subarea Decomposition

1★, 1★ Corresponding author

  1. 1Data to Knowledge (D2K) Lab, School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, INDIA.

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Priti Kumari

Data to Knowledge (D2K) Lab, School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, INDIA.

priti08.1993@gmail.com

Received: 13-10-2022; Revised: 16-04-2023; Accepted: 02-05-2023.

Volume 12, Issue 2 · pp. 383-394 · 2023 · DOI: 10.5530/jscires.12.2.034

ABSTRACT

Scientometrics indicators vary widely across subareas of the Computer Science (CS) discipline. Most researchers have previously analyzed scientometrics data specific to a particular subfield or a few subfields. More popular subareas lead to high scientometrics, and others have lower values. This work considers seven diversified CS subareas and six commonly used scientometrics indicators. First, we study the varying range of chosen scientometrics indicators of various subareas of the CS discipline. We explore the correlation patterns of these six indicators. Then, we consider a few combinations of these indicators and apply K-means clustering to decompose the pattern space. Correlation findings indicate that though the highly correlated indicators vary for most subfields, no single indicator can be considered equally suitable for all the subareas. The K-means clustering results show distinctive patterns across subfields, which are stable across K. The clustered subfield-specific indicators are quite distinct across subfields. This knowledge can be used as a signature for partitioning the subarea-specific indicators.

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    Kumari, P., & Kumar, R. (2023). Clustering Scientometrics of Computer Science Journals for Subarea Decomposition. Journal of Scientometric Research, 12(2), 383-394. https://doi.org/10.5530/jscires.12.2.034

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    © 2023 Author(s)

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. CC BY-NC-SA 4.0