A Study on Research Paper Classification Using Keyword Clustering


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 12, pp. 477-484, Dec. 2018
https://doi.org/10.3745/KTSDE.2018.7.12.477, Full Text:
Keywords: Classification Papers, K-Means Clustering, TF-IDF, Map-Reduce
Abstract

Due to the advancement of computer and information technologies, numerous papers have been published. As new research fields continue to be created, users have a lot of trouble finding and categorizing their interesting papers. In order to alleviate users’ this difficulty, this paper presents a method of grouping similar papers and clustering them. The presented method extracts primary keywords from the abstracts of each paper by using TF-IDF. Based on TF-IDF values extracted using K-means clustering algorithm, our method clusters papers to the ones that have similar contents. To demonstrate the practicality of the proposed method, we use paper data in FGCS journal as actual data. Based on these data, we derive the number of clusters using Elbow scheme and show clustering performance using Silhouette scheme.


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Cite this article
[IEEE Style]
Y. Lee, T. Pheaktra, J. Lee and J. Gil, "A Study on Research Paper Classification Using Keyword Clustering," KIPS Transactions on Software and Data Engineering, vol. 7, no. 12, pp. 477-484, 2018. DOI: https://doi.org/10.3745/KTSDE.2018.7.12.477.

[ACM Style]
Yun-Soo Lee, They Pheaktra, JongHyuk Lee, and Joon-Min Gil. 2018. A Study on Research Paper Classification Using Keyword Clustering. KIPS Transactions on Software and Data Engineering, 7, 12, (2018), 477-484. DOI: https://doi.org/10.3745/KTSDE.2018.7.12.477.