Mining research topics evolving over time using a diachronic multi-source approach

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 17-24

  Lamirel, Jean Charles ^a   Priyankar, Navesh ^b   Safi, Ghada ^c   Cuxac, Pascal ^d  

^a LORIA   (France)

^b INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

^c UNIVERSITY OF ALEPPO   (Syrian Arab Republic)

^d CNRS   (France)

Author keywords

Bayesian reasoning; Data analysis; Multi view model; Neural networks; Time varying data

Indexed keywords

BAYESIAN REASONING; CLUSTERING METHODS; DATA ANALYSIS; INCREMENTAL APPROACH; MULTI-VIEW MODELS; MULTI-VIEWS; MULTISOURCES; NEW APPROACHES; RESEARCH ACTIVITIES; RESEARCH GROUPS; RESEARCH SURVEY; RESEARCH TOPICS; SCIENTIFIC DISCIPLINE; SCIENTIFIC KNOWLEDGE; TIME VARYING DATA;

BAYESIAN NETWORKS; DATA MINING; NEURAL NETWORKS; RESEARCH; TIME VARYING NETWORKS;

DATA HANDLING;

EID: 79951742679     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2010.198     Document Type: Conference Paper

References (37)

1. Allan J., Carbonell J., Doddington, G., Yamron J., Yang Y. (1998). Topic detection and tracking pilot study, final report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, Virginia.
2. Al-Shehabi S., Lamirel J.-C. (2004). Inference Bayesian Network for Multi-topographic neural network communication: a case study in documentary data. Proceedings of ICTTA, Damas, Syria, April 2004.
3. Al-Shehabi S., Lamirel J.-C. (2005). Multi-Topographic Neural Network Communication and Generalization for Multi-Viewpoint Analysis. International Joint Conference on Neural Networks - IJCNN'05, Montreal, Canada, August 2005.
4. 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 (JIMS) 1(3) 2006.
5. Attik M., Lamirel J.-C., Al Shehabi S. (2006). Clustering Analysis for Data with Multiple Labels. Proceedings of the The IASTED International Conference on Databases and Applications (DBA), Innsbruck, Austria, February 2006.
6. Batagelj V., Zaversnik M. (2002). An o(m) algorithm for cores decomposition of networks. University of Ljubljana, preprint series Vol. 40, 799.
7. Besagni D., François C., Polanco X., Roche I., Stanalyst® : Une station pour l'analyse de l'information. Proceedings of VSST2004, Toulouse, pp 319-320, 25-29, October 2004.
8. Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3 (1974), 1-27.
9. Cuxac P., Lelu A., Cadot M. (2009). Suivi incrémental des évolutions dans une base d'information indexée : une boucle évaluation/correction pour le choix des algorithmes et des paramètres. Systèmes d'Information et Intelligence Economique (SIIE'09), 12-14 Février 2009, Hammamet, Tunisie.
10. Davies, D. and Bouldin, W. (1979). A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intell. 1 (1979) 224-227.
11. Dempster A.P., Laird N.M. and Rubin D.B. (1977). Maximum likelihood for incomplete data via the em algorithm. Journal of the Royal Statistical Society, vol. B-39: 1-38.
12. Ester M., Kriegel H.-P., Sander J., and Xu X. (1996). A Density- Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD'96). AAAI Press, Menlo Park, CA, pp. 226-231.
13. François C., Hoffmann M., Lamirel J.-C., Polanco X. (2003). Artificial Neural Network mapping experiments. EICSTES (IST- 1999-20350) Final Report (WP 9.4), 86 p., September 2003.
14. Frizke B. (1995). A growing neural gas network lerans topologies. Tesauro G., Touretzky D. S., leen T. K., Eds., Advances in neural Information processing Systems 7, pp 625-632, MIT Press, Cambridge MA.
15. Gaber M., Zaslavsky A. and Krishnaswamy S. (2005). Mining Data Streams: A Review. SIGMOD Record, 34(2).
16. 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. Atelier EvalECD'2010 Workshop, Hamamet, Tunisia.
17. Hamza H., Belaïd Y., BelaÎd. A, Chaudhuri B. B. (2008). Incremental classification of invoice documents. 19th International Conference on Pattern Recognition - ICPR 2008.
18. Kassab R., Lamirel J.-C., (2008). Feature Based Cluster Validation for High Dimensional Data. IASTED International Conference on Artificial Intelligence and Applications (AIA), Innsbruck, Austria, February 2008.
19. Kohonen T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, vol. 43, pp 56-59.
20. Lamirel J.-C., Créhange M. (1994). Application of a symbolicoconnectionist approach for the design of a highly interactive documentary database interrogation system with on-line learning capabilities. Proceedings ACM-CIKM 94, Gaitherburg, Maryland, USA, November 94.
21. Lamirel J.-C., Al-Shehabi S., François C., Hoffmann M. (2004). New classification quality estimators for analysis of documentary information: application to patent analysis and web mapping. Scientometrics, 60(3).
22. Lamirel J.-C. and Al Shehabi S. (2004). Comparison of unsupervised neural clustering methods for mining Web and textual data, SCI 2004, Orlando, FL, USA, July 2004.
23. Lamirel J.-C., Al Shehabi S. (2006). MultiSOM: a multiview neural model for accurately analyzing and mining complex data. Proceedings of the 4th International Conference on Coordinated & Multiple Views in Exploratory Visualization (CMV), London, UK, July 2006.
24. Lamirel J.-C., Ta A.P. and Attik M. (2008). Novel Labeling Strategies for Hierarchical Representation of Multidimensional Data Analysis Results. IASTED International Conference on Artificial Intelligence and Applications (AIA), Innsbruck, Austria, February 2008.
25. 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. Proceedings of IEA-AIE 2010, Cordoba, Spain, June 2010.
26. Martinetz T. et Schulten K. (1991). A "neural gas" network learns topologies. In Kohonen, T., Makisara K., Simula O., and Kangas J., editors, Articial Neural Networks, pp 397-402. Elsevier Amsterdam.
27. MacQueen J.B. (1967). Some methods of classification and analysis of multivariate observations. L. Le Cam and J. Neyman (Eds.), Proc. 5th Berkeley Symposium in Mathematics, Statistics and Probability, vol 1., pp. 281-297, Univ. of California, Berkeley, USA, 1967.
28. Merkl D., Shao Hui He, Dittenbach M., and Rauber A. (2003). Adaptive hierarchical incremental grid growing: an architecture for high-dimensional data visualization. In Proceedings of the 4th Workshop on Self-Organizing Maps, Advances in Self-Organizing Maps, pp 293-298, Kitakyushu, Japan, September 11-14 2003.
29. Mitton N., Busson A., Fleury E. (2004). Self-organization in large scale ad hoc networks. The Third Annual Mediterranean Ad Hoc Networking Workshop, (MED-HOC-NET 04). Bodrum, Turkey.
30. Polanco X., François C. (2000). Data Classification and Cluster Mapping or Visualization in Text Processing and Mining. Proceedings of the Sixth international ISKO conference, Toronto, Canada. Edited by Clare Beghtol, Lynne C. Howarth, Nancy J. Williamson: Ergon Verlag, pp 359 - 365, 10-13 July 2000.
31. P.Pons P., Latapy M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Application, 2006.
32. Prudent Y., Ennaji, A. (2005). An Incremental Growing Neural Gas learns Topology. ESANN2005, 13th European Symposium on Artificial Neural Networks, Bruges, Belgium, 27-29 April 2005.
33. Robertson, S. E., & Sparck Jones, K. (1976). Relevance Weighting of Search Terms. Journal of the American Society for Information Science, 27:129-146.
34. Schiebel E., Hörlesberger., Roche I., François C., Besagni D. (2010). An advanced diffusion model to identify emergent research issues: the case of optoelectronic devices. Scientometrics. Vol. 83, No. 3, pp 765-781, 2010.
35. Trémolières R.C. (1979). The percolation method for an efficient grouping of data. Pattern Recognition, 11(4).
36. Von Oertzen J. (2007). Results of evaluation and screening of 40 technologies. Deliverable 04 for Project PROMTECH (contract No. 15615), 32 pages + appendix.
37. Voorhees E.M. (1986). Implementing agglomerative hierarchical clustering algorithms for use in document retrieval. Information Processing and Management, vol. 22, pp 465-476.