Abstract
Proper planning of fund allocation by R&D departments of research or industrial institutes may be of key importance to increase of their revenue and future success. However, over years, making such decisions has become notoriously difficult, due to the increasing costs of research and shortening lifetime of products and processes. This is complicated even more in the light of growing specialization of research and exponential growth in number of publications. The existing bibliographic approaches used for structuring fields of research have many drawbacks, e.g. too coarse classification due to lack of the required granularity of information or incoherence of the identified thematic clusters. This paper introduces a method for identification of thematic clusters in a given research domain. The method is based on analysis of high co-occurrence frequency of word sets, which are seeds of thematic clusters. The proposed method is universal and can be applied for analysis of any research field, and the publication data extracted can be further analyzed with respect to future development trends.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Amini, M.R., Goutte, C.: A co-classification approach to learning from multilingual corpora. Machine Learning 79(1-2), 105–121 (2010)
Barnett, G.A., Huh, C., Kim, Y., Park, H.W.: Citations among communication journals and other disciplines: a network analysis. Scientometrics 88(2), 449–469 (2011)
Callon, M., Courtial, J.P., Laville, F.: Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics 22(1), 153–205 (1991)
Cambrosio, A., Limoges, C., Courtial, J.P., Laville, F.: Historical scientometrics? Mapping over 70 years of biological safety research with coword analysis. Scientometrics 27(2), 119–143 (1993)
Chang, C.T., Lai, J.Z.C., Jeng, M.D.: Fast agglomerative clustering using information of k-nearest neighbors. Pattern Recognition 43(12), 3958–3968 (2010)
Clarke, A., Gatineau, M., Thorogood, M., Wyn-Roberts, N.: Health promotion research literature in Europe 1995-2005. The European Journal of Public Health 17(suppl. 1), 24–28 (2007)
Cottrill, C.A., Rogers, E.M., Mills, T.: Co-citation analysis of the scientific literature of innovation research traditions diffusion of innovations and technology transfer. Journal of Information Science 36(3), 383–400 (2010)
Coulter, N., Monarch, I., Konda, S.: Software engineering as seen through its research literature: A study in co-word analysis. Journal of the American Society for Information Science 49(13), 1206–1223 (1998)
Ding, Y., Chowdhury, G.G., Foo, S.: Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management 37(6), 817–842 (2001)
Fabry, B., Ernst, H., Langholz, J., Köster, M.: Patent portfolio analysis as a useful tool for identifying R&D and business opportunities-an empirical application in the nutrition and health industry. World Patent Information 28(3), 215–225 (2006)
Gowda, K.C., Ravi, T.V.: Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity. Pattern Recognition Letters 16(6), 647–652 (1995)
Hung, S.W., Wang, A.P.: Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network. Scientometrics 82(1), 121–134 (2010)
Krampen, G., von Eye, A., Schui, G.: Forecasting trends of development of psychology from a bibliometric perspective. Scientometrics 87, 687–694 (2011)
Lee, P.-C., Su, H.-N., Chan, T.-Y.: Assessment of ontology-based knowledge network formation by Vector-Space Model. Scientometrics 85(3), 689–703 (2010)
Li, X., Chen, H., Huang, Z., Roco, M.C.: Patent citation network in nanotechnology (1976-2004). Journal of Nanoparticle Research 9, 337–352 (2007)
Lv, P.H., Wang, G.F., Wan, Y., Jia, L., Qing, L., Fei-Cheng, M.: Bibliometric trend analysis on global graphene research. Scientometrics 88, 399–419 (2011)
Marshakova-Shaikevich, I.: Bibliometric maps of field of science. Information Processing and Management 41(6), 1534–1547 (2005)
Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006) (2006)
Newman, M.E.J.: Power laws, Pareto distributions and Zipf’s law. Contemporary Physics 46(5), 323–351 (2005)
Noyons, E.C.M.: Bibliometric mapping of science in a science policy context. Scientometrics 50(1), 83–98 (2001)
Noyons, E.C.M., Van Raan, A.F.J.: Bibliometric cartography of scientific and technological developments of an R&D field. Scientometrics 30(1), 157–173 (1994)
Noyons, E.C.M., Van Raan, A.F.J.: Monitoring scientific developments from a dynamic perspective: Self-organized structuring to map neural network research. Journal of the American Society for Information Science 49(1), 68–81 (1998)
Peters, H.P.F., Van Raan, A.F.J.: Co-word-based science maps of chemical-engineering. Part2. Combined clustering and multidimensional scaling. Research Policy 22(1), 47–71 (1993)
Price, D.J.D.: Networks of scientific papers. Science 149(3683), 510–515 (1965)
Rajapakse, A., Titchener-Hooker, N.J., Farid, S.S.: Modelling of the biopharmaceutical drug development pathway and portfolio management. Computers & Chemical Engineering 29(6,15), 1357–1368 (2005)
Rip, A., Courtial, J.P.: Co-word maps of biotechnology: An example of cognitive scientometrics. Scientometrics 6(6), 381–400 (1984)
Sitarz, R., Kraslawski, A., Jezowski, J.: Dynamics of Knowledge Flow in Research on Distillation. Computer Aided Chemical Engineering 28, 583–588 (2010)
Tsai, H.H.: Research trends analysis by comparing data mining and customer relationship management through bibliometric methodology. Scientometrics 87, 425–450 (2011)
Van Raan, A.F.J.: Advanced bibliometric methods to assess research performance and scientific development: basic principles and recent partial applications. Research Evaluation 3(3), 151–166 (1993)
Van Raan, A.F.J., Tijssen, R.J.W.: The neural net of neural network research: An exercise In bibliometric mapping. Scientometrics 26(1), 169–192 (1993)
Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval. Information Processing & Management 22(6), 465–476 (1986)
Wang, Z.Y., Li, G., Li, C.Y., Li, A.: Research on the semantic-based co-word analysis. Scientometrics 90, 855–875 (2012)
Yang, Y., Wu, M., Cui, L.: Integration of three visualization methods based on co-word analysis. Scientometrics 90, 659–673 (2012)
Yi, S., Choi, J.: The organization of scientific knowledge: the structural characteristics of keyword networks. Scientometrics 90, 1015–1026 (2012)
Zapata, J.C., Varma, V.A., Reklaitis, G.V.: Impact of tactical and operational policies in the selection of a new product portfolio. Computers & Chemical Engineering 32(1-2), 307–319 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Sitarz, R., Heneczkowski, M., Jabłońska-Sabuka, M., Krasławski, A. (2015). Clustering Method for Analysis of Research Fields: Examples of Composites, Nanocomposites and Blends. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-11310-4_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
eBook Packages: EngineeringEngineering (R0)