A new approach for automatizing the analysis of research topics dynamics: application to optoelectronics research
- 296 Downloads
The objective of this paper is to propose a new unsupervised incremental approach in order to follow the evolution of research themes for a given scientific discipline in terms of emergence or decline. Such behaviors are detectable by various methods of filtering. However, our choice is made on the exploitation of neural clustering methods in a multi-view context. This new approach makes it possible to take into account the incremental and chronological aspects of information by opening the way to the detection of convergences and divergences of research themes at a large scale.
KeywordsDiachronic analysis Clustering Multiple viewpoint analysis Unsupervised learning Bayesian reasoning Neural networks
The author wishes to thanks Pascal Cuxac (INIST-CNRS) for his valuable help in the results validation task.
- Al Shehabi, S., Lamirel, J.-C. (2004). Inference Bayesian Network for Multi-topographic neural network communication: A case study in documentary data. In Proceedings of ICTTA, Damas, Syria, April 2004.Google Scholar
- Al Shehabi, S., Lamirel, J.-C. (2006). Evaluation of collaboration between European universities using dynamic interaction between multiple sources. Journal of Information Management and Scientometrics, 1(3).Google Scholar
- Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y. (1998). Topic detection and tracking pilot study, final report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, Virginia.Google Scholar
- Attik, M., Lamirel, J.-C., Al Shehabi, S. (2006). Clustering analysis for data with multiple labels. In Proceedings of the The IASTED International Conference on Databases and Applications (DBA), Innsbruck, Austria, February 2006.Google Scholar
- François, C., Hoffmann, M., Lamirel, J.-C., Polanco, X. (2003). Artificial Neural Network mapping experiments. EICSTES (IST-1999-20350) Final Report (WP 9.4), September 2003.Google Scholar
- Frizke, B. (1995). A growing neural gas network learns topologies. In G Tesauro, D. S Touretzky, T. K leen (Eds.), Advances in neural Information processing Systems 7 (pp. 625–632). Cambridge: MIT Press.Google Scholar
- Gaber, M., Zaslavsky, A., Krishnaswamy, S. (2005). Mining data streams: A review. SIGMOD Record, 34(2).Google Scholar
- Ghribi, M., Cuxac, P., Lamirel, J. C., Lelu, A. (2010). Mesures de qualité de clustering de documents: Prise en compte de la distribution des mots-clés. In EvalECD’2010 Workshop, Hamamet, Tunisia.Google Scholar
- Lamirel, J.-C., & Al Shehabi, S. (2004b). Comparison of unsupervised neural clustering methods for mining Web and textual data. In SCI 2004, Orlando, FL, USA, July 2004.Google Scholar
- Lamirel, J.-C., Créhange, M. (1994). Application of a symbolico-connectionist approach for the design of a highly interactive documentary database interrogation system with on-line learning capabilities. In Proceedings ACM-CIKM 94, Gaitherburg, MD, USA, November 1994.Google Scholar
- Lamirel, J.-C., Ta, A. P., & Attik M. (2008). Novel labeling strategies for hierarchical representation of multidimensional data analysis results. In IASTED International Conference on Artificial Intelligence and Applications (AIA), Innsbruck, Austria, February 2008.Google Scholar
- Lamirel, J.-C., Boulila, Z., Ghribi, M., Cuxac, P. (2010). A new incremental growing neural gas algorithm based on clusters labeling maximization: application to clustering of heterogeneous textual data. In Proceedings of IEA-AIE 2010, Cordoba, Spain, June 2010.Google Scholar
- Lamirel, J.-C., Mall, R., Cuxac, P., Safi, G. (2011). Variations to incremental growing neural gas algorithm based on label maximization. In Proceedings of IJCNN 2011, San José, CA, USA, August 2011.Google Scholar
- MacQueen, J. B. (1967). Some methods of classification and analysis of multivariate observations. In L. Le Cam & J. Neyman (Eds.), Proceedings 5th Berkeley Symposium in Mathematics, Statistics and Probability (Vol 1, pp. 281–297), University of California, Berkeley, USA, 1967.Google Scholar
- Results (2011). https://sites.google.com/site/diacresults2012.
- Thijs, B., Glänzel, W. (2010). A new hybrid approach for bibliometrics aided retrieval. In Sixth International Conference on Webometrics, Informetrics & Scientometrics, and 11th COLLNET Meeting, Mysore, India, October 2010.Google Scholar