J. M. Aguilar and W. D. Ross, Incremental ART: a neural network system for recognition by incremental feature extraction, Proceedings of the World Congress on Neural Networks (WCNN-94), 1994.

M. Ankerst, M. M. Breunig, H. P. Kriegel, and J. Sander, OPTICS: ordering points to identify the clustering structure, Proceedings of the ACM SIGMOD'99 Int. Conf. on Management of Data, 1999.

H. Azzag, F. Picarougne, C. Guinot, and G. Venturini, Un survol des algorithmes biomimétiques pour la classification', Revue des Nouvelles Technologies de l'Information (RNTI-C-1), Classification et, pp.13-24, 2004.

H. Azzag, F. Picarougne, C. Guinot, and G. Venturini, Classification de données par automate cellulaire, 12èmes Rencontres de la Société Francophone de Classification (SFC2005), pp.47-50, 2005.

V. Batagelj and M. Zaversnik, An o(m) Algorithm for Cores Decomposition of Networks, pp.798-806, 2002.

H. Binztock and P. Gallinari, Un algorithme en ligne pour la détection de nouveauté dans un flux de documents, 2002.

W. L. Buntine, Variational Extensions to EM and Multinomial PCA, pp.23-34, 2002.
DOI : 10.1007/3-540-36755-1_3
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.5167

M. Cadot, Extraire et Valider les Relations Complexes en Sciences Humaines: Statistique, Motifs et Règles d'Association, 2006.

M. Cadot, P. Cuxac, and A. Lelu, Random simulations of a datatable for efficiently mining reliable and non-redundant itemsets, ASMDA2007 Conference (International Conference on Applied Stochastic Models and Data Analysis), 2007.
URL : https://hal.archives-ouvertes.fr/inria-00186100

M. Cadot and A. Lelu, A novel decomposition algorithm for binary datatables: encouraging results on discrimination tasksComparison of the maps of science, Proceedings RCIS 2010 ? Fourth International Conference on Research Challenges in Information Science, pp.373-387, 2000.

C. C. Chen, Y. T. Chen, Y. S. Sun, and M. C. Chen, Life Cycle Modeling of News Events Using Aging Theory, pp.47-59, 2003.
DOI : 10.1007/978-3-540-39857-8_7

D. Domengès and M. Volle, Analyse factorielle sph??rique: Une exploration, Annales de l'ins????, vol.35, issue.35, pp.3-84, 1979.
DOI : 10.2307/20075327

M. Ester, H. Kriegel, J. Sander, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD'96), pp.226-231, 1996.

D. Fisher, Knowledge acquisition via incremental conceptual clustering, Machine Learning, pp.139-172, 1987.
DOI : 10.1007/BF00114265
URL : http://axon.cs.byu.edu/~martinez/classes/678/Papers/Fisher_Cobweb.pdf

M. Gaber, A. Zaslavsky, and S. Krishnaswamy, Mining data streams, SIGMOD Record, 2005.
DOI : 10.1145/1083784.1083789

R. Gaudin and N. Nicoloyannis, Apprentissage non-supervise de séries temporelles à l'aide des k-Means et d'une nouvelle méthode d'agrégation de séries', 5èmes journées d'Extraction et Gestion des Connaissances, pp.201-21219, 2005.

A. Guénoche, Clustering by vertex density in a graph Meeting of the International Federation of the Classification Societies, pp.15-23, 2004.

S. Guha, R. Rastogi, and K. Shim, CURE: an efficient clustering algorithm for large databases, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp.73-84, 1998.

S. Hader and F. A. Hamprecht, Efficient density clustering using basin spanning trees', Between Data Science and Applied Data Analysis, pp.39-48, 2003.
DOI : 10.1007/978-3-642-18991-3_5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.11.7620

H. Hamza, Y. Belaïd, A. Belaïd, and B. B. Chaudhuri, An End-to-End Administrative Document Analysis System, 2008 The Eighth IAPR International Workshop on Document Analysis Systems, 2008.
DOI : 10.1109/DAS.2008.43
URL : https://hal.archives-ouvertes.fr/inria-00346019

G. Hébrail, F. Clérot, and B. Csernel, Atelier Gestion et fouille de flux de données, 2007.

A. Hinneburg and D. A. Keim, An efficient approach to clustering in large multimedia databases with noise, Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, pp.58-65, 1998.

P. Humbert, C. François, P. Cuxac, and A. David, La visualisation dynamique de connaissances scientifiques: intégration des besoins des utilisateurs, Canadian Journal of Information and Library Science, vol.30, issue.12, pp.38-55, 2007.

G. Karypis, E. H. Han, and V. Kumar, Chameleon: hierarchical clustering using dynamic modeling, Computer, vol.32, issue.8, 1999.
DOI : 10.1109/2.781637

J. Lavergne, H. Azzag, C. Guinot, and G. Venturini, Construction incrémentale et visualisation de graphes de voisinage par des fourmis artificielles, RNTI-E-9 Cépaduès-Éditions, pp.135-146, 2007.

C. Lee and E. Antonsson, Dynamic partitional clustering using evolution strategies, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies and Industrial Opportunities (Cat. No.00CH37141), 2000.
DOI : 10.1109/IECON.2000.972427
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.9974

A. Lelu, Clusters and factors: neural algorithms for a novel representation of huge and highly multidimensional data sets, New Approaches in Classification and Data Analysis, pp.241-248, 1994.
DOI : 10.1007/978-3-642-51175-2_27

A. Lelu, Evaluation de trois mesures de similarité utilisées en sciences de l'information Information Sciences for Decision Making, 2003.

A. Lelu, Clustering dynamique d'un flot de données: un algorithme incrémental et optimal de détection des maxima de densité, 8e Journées EGC 2006 (Extraction et Gestion de Connaissances) RNTI-E6, Cepadues publ, pp.35-40, 2006.

A. Lelu, P. Cuxac, and J. Johansson, Classification dynamique d'un flux documentaire: une évaluation statique préalable de l'algorithme GERMEN, Journées internationales d'Analyse statistique des Données Textuelles (JADT), pp.617-630, 2006.

D. D. Lewis, Y. Yang, T. Rose, and F. Li, A new benchmark collection for text categorization research, Journal of Machine Learning Research, vol.5, pp.361-397, 2004.

J. Lin, M. Vlachos, E. Keogh, and D. Gunopulos, Iterative Incremental Clustering of Time Series, Lecture Notes in Computer Science, vol.2992, pp.106-122, 2004.
DOI : 10.1007/978-3-540-24741-8_8

D. Memmi, Incremental clustering and category drift', Les cahiers du laboratoire Leibniz, 2001.

E. Mfoumoune, Les Aspects Algorithmiques de la Classification Ascendante Pyramidale et Incrémentale, p.220, 1998.

N. Mitton, E. Fleury, I. Guérin-lassous, and S. Tixeuil, Auto-stabilisation dans les réseaux ad hoc', 7èmes Rencontres Francophones sur les aspects Algorithmiques des Télécommunications, 2005.

N. Monmarché, D. Laügt, M. Mestre, N. Labroche, A. Oliver et al., Classification et visualisation dynamique de données par nuage d'insectes volants', Neuvièmes rencontres de la Société Francophone de Classification, pp.254-261, 2001.

J. Moody, Peer influence groups: identifying dense clusters in large networks, Social Networks, vol.23, issue.4, pp.261-283, 2001.
DOI : 10.1016/S0378-8733(01)00042-9

A. Pons-porrata, R. Berlanga-llavori, J. Ruiz-shulcloper, and J. M. Perez-martinez, JERARTOP: A New Topic Detection System, Lecture Notes in Computer Sciences ? 9th Iberoamerican Congress on Pattern Recognition, pp.446-453, 2004.
DOI : 10.1007/978-3-540-30463-0_56

Y. Prudent and A. Ennaji, Extraction incrémentale de la topologie des Données', 11èmes Rencontres de la Société Francophone de Classification, pp.278-281, 2004.

D. Puzenat, Priming an artificial neural classifier, 1995.
DOI : 10.1007/3-540-59497-3_223

A. K. Suerdem and K. Karabekir, Bridging qualitative and quantitative methods for classifying policy actors into policy discourse communities: thematic analysis and formal concept analysis approaches, International Journal of Data Analysis Techniques and Strategies, vol.2, issue.3, pp.199-216, 2010.
DOI : 10.1504/IJDATS.2010.034056

R. C. Tremolières, The percolation method for an efficient grouping of data, Pattern Recognition, vol.11, issue.4, pp.255-262, 1979.
DOI : 10.1016/0031-3203(79)90035-9

R. Wan, J. Chen, L. Wang, and X. Su, Grid-based clustering over an evolving data stream, International Journal of Data Mining, Modelling and Management, vol.1, issue.4, pp.393-490, 2009.
DOI : 10.1504/IJDMMM.2009.029033

C. L. Wayne, Topic detection & tracking (TDT), overview & perspective, Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 1998.

L. Yu, L. Gao, D. Wang, and S. Fu, Quantitative function for community structure detection, International Journal of Data Mining, Modelling and Management, vol.2, issue.4, pp.351-368, 2010.
DOI : 10.1504/IJDMMM.2010.035563

=. Cij and . Mi, Mj / N (for more information please refer to Cadot, 2006.

D. Lewis, RCV1-v2/LYRL2004: The LYRL2004 Distribution of the RCV1-v2 Text Categorisation Test Collection, 2004.