Abstract
This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic, non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography of bibliographic data. The ANNs tested are: the Adaptive Resonance Theory (ART 1), a Multilayer Perceptron (MLP), and an associative network with unsupervised learning (KOHONEN). This platform is intended for quantitative analysis of information.
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References
Campanario, J. M. (1995), Using neural networks to study networks of scientific journals,Scientometrics, 33:23–40.
Carpenter, G. A., Grossberg, S. (1988), The ART of adaptative pattern recognition by a self-organizing neural network,Computer, 21: 77–88.
Cheng, B., Titterington, D. M. (1994), Neural networks: A review from statistical perspective,Statistical Science, 9 (1): 2–54.
Doszkocs, T. E.;Reggia, J.;Lin, X. (1990), Connectionist models and information retrieval,Annual Review of Information Science and Technology, 25: 209–260.
Grivel, L., Polanco, X.; Kaplan, A. (1997), A computer system for big scientometrics at the age of the World Wide Web,Sixth International Conference on Scientometrics and Informatrics, June, 16–19 1997, Jerusalem, Israel, 131–142.
Hertz, J., Krogh, A.;Palmer R.G. (1991),Introduction to the Theory of Neural Computation, MA, Addison-Wesley Reading.
Jodouin, J. F. (1994),Les Réseaux Neuromimétiques. Paris, Hermès.
Keim, J-Ph. (1996),Mise en oeuvre de réseaux neuronaux dans un logiciel d'analyse de l'information scientifique et technique, Technical report (INIST-ISIAL), Nancy, France.
Kohonen, T. (1982), Self-organized formation of topologically correct feature maps,Biol. Cybern., 43: 59–69.
Kohonen, T. (1989),Self-organisation and associative memory, Berlin, Springer Verlag.
Kohonen, T. (1995),Self-organizing maps, Springer, Berlin.
Lebart, L., Morineau, A.;Piron, M. (1995),Statistique Exploratoire Multidimensionnelle, Paris, DUNOD.
Lelu, A. (1991), From data analysis to neural networks: New prospects for efficient browsing through databases,Journal of Information Science, 17: 1–12.
Lelu, A. (1993),Modèles Neuronaux pour l'Analyse de Données Documentaires et Textuelles, Doctoral thesis from the Université de Paris 6.
Lelu, A., François, C. (1992A), Information retrieval based on a neural unsupervised extraction of thematic fuzzy clusters.Conférence “Les Réseaux Neuro-Mimetiques et leurs Applications”, 2–6 November, Nimes, France.
Lelu, A., François, C. (1992B), Hypertext paradigm in the field of information retrieval: A neural approach,4th ACM Conference on Hypertext, 30 November–4 December, Milan, Italy.
MacQueen, J. (1967), Some methods for classification and analysis of multivariate observations,Proc. 5th Berkeley Symp. Math. Proba.: 281–297.
Polanco, X. (1996), La notion d'analyse de l'information dans le domaine de l'information scientifique et technique.Conférence INRA—Information scientifique et technique, 21–23 october, Tours, France (forthcoming).
Price, D. De S. (1963),Little Science, Big Science, New York, Columbia University Press.
Ripley, B. D. (1994), Neural networks and related methods of classification,Journal of the Royal Statistical Society, Ser. B, 56 (3): 409–456.
Van Raan, A. F. J.;Tijssen, R. J. W.(1993), The neural net of neural network research. An exercise in bibliometric mapping,Scientometrics, 26: 109–192.
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Polanco, X., François, C. & Keim, J.P. Artificial neural network technology for the classification and cartography of scientific and technical information. Scientometrics 41, 69–82 (1998). https://doi.org/10.1007/BF02457968
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DOI: https://doi.org/10.1007/BF02457968