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Multi-view clustering with exemplars for scientific mapping

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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.

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Authors and Affiliations

  1. Key Laboratory of Machine Perception (Ministry of Education), Center for Information Science, Peking University, 100871, Beijing, China

    Xiangfeng Meng, YunHai Tong & Shaohua Tan

  2. Credit Reference Center, People’s Bank of China, Beijing, China

    Xinhai Liu

  3. Center of Financial Intelligence Research, Peking University, Beijing, China

    Xinhai Liu

  4. Department of MSI, Center for R&D Monitoring (ECOOM), Katholieke Universiteit Leuven, Waaistraat 6, 3000, Leuven, Belgium

    Wolfgang Glänzel

  5. IRPS, Hungarian Academy of Sciences, Budapest, Hungry

    Wolfgang Glänzel

Authors
  1. Xiangfeng Meng
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  2. Xinhai Liu
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  3. YunHai Tong
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  4. Wolfgang Glänzel
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  5. Shaohua Tan
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Correspondence to Xinhai Liu.

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Meng, X., Liu, X., Tong, Y. et al. Multi-view clustering with exemplars for scientific mapping. Scientometrics 105, 1527–1552 (2015). https://doi.org/10.1007/s11192-015-1682-7

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  • DOI: https://doi.org/10.1007/s11192-015-1682-7

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