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Hybrid clustering of multi-view data via Tucker-2 model and its application

Author

Listed:
  • Xinhai Liu

    (Wuhan University of Science and Technology
    Katholieke Universiteit Leuven)

  • Wolfgang Glänzel

    (Katholieke Universiteit Leuven
    Hungarian Academy of Sciences)

  • Bart De Moor

    (Katholieke Universiteit Leuven)

Abstract

With the modern technology fast developing, most of entities can be observed by different perspectives. These multiple view information allows us to find a better pattern as long as we integrate them in an appropriate way. So clustering by integrating multi-view representations that describe the same class of entities has become a crucial issue for knowledge discovering. We integrate multi-view data by a tensor model and present a hybrid clustering method based on Tucker-2 model, which can be regarded as an extension of spectral clustering. We apply our hybrid clustering method to scientific publication analysis by integrating citation-link and lexical content. Clustering experiments are conducted on a large-scale journal set retrieved from the Web of Science (WoS) database. Several relevant hybrid clustering methods are cross compared with our method. The analysis of clustering results demonstrate the effectiveness of the proposed algorithm. Furthermore, we provide a cognitive analysis of the clustering results as well as the visualization as a mapping of the journal set.

Suggested Citation

  • Xinhai Liu & Wolfgang Glänzel & Bart De Moor, 2011. "Hybrid clustering of multi-view data via Tucker-2 model and its application," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 819-839, September.
    • Handle: RePEc:spr:scient:v:88:y:2011:i:3:d:10.1007_s11192-011-0348-3
    DOI: 10.1007/s11192-011-0348-3
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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. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    3. He, Xiaofeng & Zha, Hongyuan & H.Q. Ding, Chris & D. Simon, Horst, 2002. "Web document clustering using hyperlink structures," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 19-45, November.
    4. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    5. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    6. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
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    Cited by:

    1. Lin Zhu & Junjie Zhang & Scott W. Cunningham, 2022. "Domain expertise extraction for finding rising stars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5475-5495, September.

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