1. H. Attias, A variational Bayesian framework for graphical models, Advances in Neural Information Processing Systems, vol. 12, MIT Press, Cambride, MA, 2000.
2. H. Azzag, Classification hiérarchique par des fourmis artificielles: application à la fouille de données et de textes pour le Web, Ph.D. Thesis, Ecole Doctorale Santé Sciences et Technologies, Université François Rabelais Tours, 2005.
3. B. Babcock, M. Datar, R. Motwani, L. O’Callaghan, Maintaining variance and k-medians over data stream windows, in: Proceedings of the 22nd ACM SIGMOD-SIGACT Symposium on Principles of Database Systems, 2003, pp. 234–243.
4. S. Basu, M. Bilenko, R. J. Mooney, A probabilistic framework for semi-supervised clustering, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 59–68.
5. The art and science of dynamic network visualization;Bender-deMoll;Journal of Social Structure,2006