Hybrid self-optimized clustering model based on citation links and textual features to detect research topics

PloS One
Dejian YuRongyu Liu

Abstract

The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify "core documents". First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmar...Continue Reading

References

Jul 13, 2004·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·M E J Newman
May 26, 2006·Proceedings of the National Academy of Sciences of the United States of America·M E J Newman
Jul 30, 2014·IEEE Transactions on Cybernetics·Yizhang JiangPengjiang Qian
Aug 28, 2015·PloS One·Diego Raphael Amancio

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