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Multi-view clustering with exemplars for scientific mapping

Author

Listed:
  • Xiangfeng Meng

    (Peking University)

  • Xinhai Liu

    (People’s Bank of China
    Peking University)

  • YunHai Tong

    (Peking University)

  • Wolfgang Glänzel

    (Katholieke Universiteit Leuven
    Hungarian Academy of Sciences)

  • Shaohua Tan

    (Peking University)

Abstract

Scientific mapping has now become an important subject in the scientometrics field. Journal clustering can provide insights into both the internal relations among journals and the evolution trend of studies. In this paper, we apply the affinity propagation (AP) algorithm to do scientific journal clustering. The AP algorithm identifies clusters by detecting their representative points through message passing within the data points. Compared with other clustering algorithms, it can provide representatives for each cluster and does not need to pre-specify the number of clusters. Because the input of the AP algorithm is the similarity matrix among data points, it can be applied to various forms of data sets with different similarity metrics. In this paper, we extract the similarity matrices from the journal data sets in both cross citation view and text view and use the AP algorithm to cluster the journals. Through empirical analysis, we conclude that these two clustering results by the two single views are highly complementary. Therefore, we further combine text information with cross citation information by using the simple average scheme and apply the AP algorithm to conduct multi-view clustering. The multi-view clustering strategy aims at obtaining refined clusters by integrating information from multiple views. With text view and citation view integrated, experiments on the Web of Science journal data set verify that the AP algorithm obtains better clustering results as expected.

Suggested Citation

  • Xiangfeng Meng & Xinhai Liu & YunHai Tong & Wolfgang Glänzel & Shaohua Tan, 2015. "Multi-view clustering with exemplars for scientific mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1527-1552, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1682-7
    DOI: 10.1007/s11192-015-1682-7
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    References listed on IDEAS

    as
    1. Xinhai Liu & Shi Yu & Frizo Janssens & Wolfgang Glänzel & Yves Moreau & Bart De Moor, 2010. "Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(6), pages 1105-1119, June.
    2. Zhang, Lin & Liu, Xinhai & Janssens, Frizo & Liang, Liming & Glänzel, Wolfgang, 2010. "Subject clustering analysis based on ISI category classification," Journal of Informetrics, Elsevier, vol. 4(2), pages 185-193.
    3. Loet Leydesdorff, 2006. "Can scientific journals be classified in terms of aggregated journal‐journal citation relations using the Journal Citation Reports?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(5), pages 601-613, March.
    4. Kevin W. Boyack & Katy Börner & Richard Klavans, 2009. "Mapping the structure and evolution of chemistry research," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 45-60, April.
    5. Xinhai Liu & Wolfgang Glänzel & Bart Moor, 2012. "Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 473-493, May.
    6. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    7. Loet Leydesdorff & Ismael Rafols, 2009. "A global map of science based on the ISI subject categories," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 348-362, February.
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    Cited by:

    1. Fabian Meyer-Brötz & Edgar Schiebel & Leo Brecht, 2017. "Experimental evaluation of parameter settings in calculation of hybrid similarities: effects of first- and second-order similarity, edge cutting, and weighting factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1307-1325, June.
    2. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "Hybrid self-optimized clustering model based on citation links and textual features to detect research topics," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.

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