Abstract
The purpose of this study was to propose a multi-layer topic map analysis of palliative care research using co-word analysis of informetrics with Growing Hierarchical Self-Organizing Map (GHSOM). The topic map illustrated the delicate intertwining of subject areas and provided a more explicit illustration of the concepts within each subject area. We applied GHSOM, a text-mining Neural Networks tool, to obtain a hierarchical topic map. The result of the topic map may indicate that the subject area of health care science and service played an importance role in multidiscipline within the research related to palliative care.
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References
Cameron, C.S.: Professional Attitudes and Terminal Care. Public Health Rep. 67, 955–959 (1952)
Payne, S.A., Turner, J.M.: Research methodologies in palliative care: a bibliometric analysis. Palliat 22, 336–342 (2008)
Dittenbach, M., Rauber, A., Merkl, D.: Uncovering hierarchical structure in data using the growing hierarchical self-organizing map. Neurocomputing 48, 199–216 (2002)
Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13, 1331–1341 (2002)
Campanario, J.: Using neural networks to study networks of scientific journals. Scientometrics 33, 23–40 (1995)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological cybernetics 43, 59–69 (1982)
Kohonen, T., Kaski, S., Lagus, K., Salojarvi, J., Honkela, J., Paatero, V., Saarela, A.: Self organization of a massive document collection. IEEE Transactions on Neural Networks 11, 574–585 (2000)
Noyons, E., van Raan, A.: Monitoring scientific developments from a dynamic perspective: self-organized structuring to map neural network research. Journal of the American Society for Information Science 49, 68–81 (1998)
Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13, 1331 (2002)
Shih, J., Chang, Y., Chen, W.: Using GHSOM to construct legal maps for Taiwan’s securities and futures markets. Expert Systems with Applications 34, 850–858 (2008)
Li, S.T., Chang, W.C.: Design And Evaluation Of A Layered Thematic Knowledge Map System. Journal of Computer Information Systems 49 (2009)
Wolfram, D.: Applied informetrics for information retrieval research. Greenwood Publishing Group, Westport (2003)
Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley, Reading (1989)
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© 2011 Springer-Verlag Berlin Heidelberg
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Yang, YH., Bhikshu, H., Tsaih, RH. (2011). The Topic Analysis of Hospice Care Research Using Co-word Analysis and GHSOM. In: Chen, R. (eds) Intelligent Computing and Information Science. ICICIS 2011. Communications in Computer and Information Science, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18129-0_72
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DOI: https://doi.org/10.1007/978-3-642-18129-0_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18128-3
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