Paving the way for next generation data-stream clustering: Towards a unique and statistically valid cluster structure at any time step

International Journal of Data Mining, Modelling and Management

Volumn 3, Issue 4, 2011, Pages 341-360

  Cuxac, Pascal ^a   Lelu, Alain ^b,c   Cadot, Martine ^c,d  

^a INIST CNRS   (France)

^b UNIVERSITÉ DE FRANCHE COMTÉ   (France)

^c LORIA   (France)

^d UHP Campus Scientifique ^*  (France)

Author keywords

Data mining; Data stream clustering; Density mode clustering; Graph validation; Incremental algorithm; Randomisation test; Text mining

Indexed keywords

EID: 84858396945     PISSN: 17591163     EISSN: 17591171     Source Type: Journal    
DOI: 10.1504/IJDMMM.2011.042933     Document Type: Article

References (47)

1. Aguilar, J. M. and Ross, W. D. (1994) 'Incremental ART: a neural network system for recognition by incremental feature extraction', Proceedings of the World Congress on Neural Networks (WCNN-94).
2. Ankerst, M., Breunig M. M., Kriegel, H. P. and Sander, J. (1999) 'OPTICS: ordering points to identify the clustering structure', Proceedings of the ACM SIGMOD'99 Int. Conf. on Management of Data, Philadelphia PA.
3. Azzag, H., Picarougne, F., Guinot, C. and Venturini, G. (2004) 'Un survol des algorithmes biomimétiques pour la classification', Revue des Nouvelles Technologies de l'Information (RNTI-C-1), Classification et fouille de données, pp. 13-24, Cépaduès édition.
4. Azzag, H., Picarougne, F., Guinot, C. and Venturini, G. (2005) 'Classification de données par automate cellulaire', 12èmes Rencontres de la Société Francophone de Classification (SFC2005), pp. 47-50, Montréal, Canada.
5. Batagelj, V. and Zaversnik, M. (2002) An o (m) Algorithm for Cores Decomposition of Networks, Vol. 40, pp. 798-806, University of Ljubljana, preprint series.
6. Binztock, H. and Gallinari, P. (2002) 'Un algorithme en ligne pour la détection de nouveauté dans un flux de documents', in Morin, A. and Sébillot, P. (Eds.): JADT'2002, IRISA, Saint Malo.
7. Buntine, W. L. (2002) 'Variational extensions to EM and multinomial PCA', ECML 2002, pp. 23-34.
8. Cadot, M. (2006) Extraire et Valider les Relations Complexes en Sciences Humaines: Statistique, Motifs et Règles d'Association, PhD thesis, Université de Franche-Comté, Besançon, France.
9. Cadot, M., Cuxac, P. and Lelu, A. (2007) 'Random simulations of a datatable for efficiently mining reliable and non-redundant itemsets', ASMDA2007 Conference (International Conference on Applied Stochastic Models and Data Analysis), Chania, Greece.
10. Cadot, M. and Lelu, A. (2010) 'A novel decomposition algorithm for binary datatables: encouraging results on discrimination tasks', in Proceedings RCIS 2010 - Fourth International Conference on Research Challenges in Information Science, Nice, France
11. Cahlik, T. (2000) 'Comparison of the maps of science', Scientometrics, Vol. 19, No. 3, pp. 373-387.
12. Chen, C. C., Chen, Y. T., Sun, Y. S. and Chen, M. C. (2003) 'Life cycle modeling of news events using aging theory', ECML 2003, pp. 47-59. (Pubitemid 37230965)
13. Domengès, D. and Volle, M. (1979) 'Analyse factorielle sphérique: une exploration', Annales de l'INSEE, Vol. 35, pp. 3-84, INSEE, Paris.
14. Ester, M., Kriegel, H-P., Sander, J. and Xu, X. (1996) '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, AAAI Press, Menlo Park, CA.
15. Fisher, D. (1987) 'Knowledge acquisition via incremental conceptual clustering', Machine Learning, Vol. 2, pp. 139-172.
16. Gaber, M., Zaslavsky, A. and Krishnaswamy, S. (2005) 'Mining data streams: a review', SIGMOD Record, Vol. 34, p. 2.
17. Gaudin, R. and Nicoloyannis, N. (2005) '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 (EGC 2005), pp. 201-212, pp. 19-21, Janvier, Paris.
18. Guénoche, A. (2004) 'Clustering by vertex density in a graph', in Mining, D. and Banks et al. (Eds.): Meeting of the International Federation of the Classification Societies, pp. 15-23, Classification, Clustering and Data, Springer, Chicago.
19. Guha, S., Rastogi, R. and Shim, K. (1998) 'CURE: an efficient clustering algorithm for large databases', Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 73-84, New York. (Pubitemid 128655958)
20. Hader, S. and Hamprecht F. A. (2003) 'Efficient density clustering using basin spanning trees', Between Data Science and Applied Data Analysis, pp. 39-48, Springer.
21. Hamza, H., Belaïd, Y., Belaïd, A. and Chaudhuri, B. B. (2008) 'An end-to-end administrative document analysis system', Eight IAPR International Workshop on Document Analysis Systems (DAS2008), Nara, Japan.
22. Hébrail, G., Clérot, F. and Csernel, B. (2007) 'Atelier Gestion et fouille de flux de données', EGC 2007, Namur, Belgium.
23. Hinneburg, A. and Keim, D. A. (1998) 'An efficient approach to clustering in large multimedia databases with noise', Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, (KDD98), pp. 58-65, New York.
24. Humbert, P., François, C., Cuxac, P. and David, A., (2007) 'La visualisation dynamique de connaissances scientifiques: intégration des besoins des utilisateurs', Canadian Journal of Information and Library Science, Vol. 30, Nos. 1/2, pp. 38-55.
25. Karypis, G., Han, E. H. and Kumar, V. (1999) Chameleon: A Hierarchical Clustering Algorithm using Dynamic Modeling, Technical Report TR-99-007, Department of Computer Science, University of Minnesota, Minneapolis.
26. Lavergne, J., Azzag, H., Guinot, C. and Venturini, G. (2007) 'Construction incrémentale et visualisation de graphes de voisinage par des fourmis artificielles', 7èmes journées d'Extraction et Gestion des Connaissances (EGC 2007), Vol. 1, pp. 135-146, Namur. RNTI-E-9 Cépaduès-Éditions 2007.
27. Lebart, L., Morineau, A. and Tabard, N. (1982) Techniques de la Description Statistique, Dunod, Paris.
28. Lee, C-Y. and Antonsson, E. (2000) 'Dynamic partitional clustering using evolution strategies', Proceedings of the Third Asia-Pacific Conference on Simulated Evolution and Learning, Nagoya, Japan.
29. Lelu, A. (1994) 'Clusters and factors: neural algorithms for a novel representation of huge and highly multidimensional data sets', in Diday, E. and Lechevallier, Y. et al. (Eds.): New Approaches in Classification and Data Analysis, pp. 241-248, Springer-Verlag, Berlin.
30. Lelu, A. (2003) 'Evaluation de trois mesures de similarité utilisées en sciences de l'information', in Rostaing, H. (Ed.): Information Sciences for Decision Making, No. 6.
31. Lelu, A. (2006) '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), Vol. 2, pp. 35-40, in RNTI-E6, Cepadues publ., Toulouse, France.
32. Lelu, A., Cuxac, P. and Johansson, J. (2006) '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, Besançon.
33. Lewis, D. D., Yang, Y., Rose, T. and Li, F. RCV1 (2004) 'A new benchmark collection for text categorization research', Journal of Machine Learning Research, Vol. 5, pp. 361-397.
34. Lin, J., Vlachos, M., Keogh, E. and Gunopulos, D. (2004) 'Data mining and knowledge discovery - Iterative incremental clustering of time series', Lecture Notes in Computer Science, Vol. 2992, pp. 106-122.
35. Memmi, D. (2001) 'Incremental clustering and category drift', Les cahiers du laboratoire Leibniz, No. 22, France.
36. Mfoumoune, E. (1998) Les Aspects Algorithmiques de la Classification Ascendante Pyramidale et Incrémentale, p. 220, PhD thesis, Université Paris IX Dauphine (E. DIDAY), France.
37. Mitton, N., Fleury, E., Guérin-Lassous, I. and Tixeuil, S. (2005) 'Auto-stabilisation dans les réseaux ad hoc', 7èmes Rencontres Francophones sur les aspects Algorithmiques des Télé communications, ALGOTEL 2005, Presqu'île de Giens, France.
38. Monmarché, N., Laügt, D., Mestre, M., Labroche, N., Oliver, A. and Venturini, G. (2001) 'Classification et visualisation dynamique de données par nuage d'insectes volants', Neuvièmes rencontres de la Société Francophone de Classification, pp. 254-261, Pointe à Pitre (Guadeloupe).
39. Moody, J. (2001) 'Identifying dense clusters in large networks', Social Networks, Vol. 23, pp. 261-283.
40. Pons-Porrata, A., Berlanga-Llavori, R., Ruiz-Shulcloper, J. and Perez-Martinez, J. M. (2004) 'JERARTOP: A new topic detection system', Lecture Notes in Computer Sciences - 9th Iberoamerican Congress on Pattern Recognition, Vol. 3287, pp. 446-453. (Pubitemid 39741533)
41. Prudent, Y. and Ennaji, A. (2004) 'Extraction incrémentale de la topologie des Données', 11èmes Rencontres de la Société Francophone de Classification, pp. 278-281.
42. Puzenat, D. (1995) Priming an Artificial Neural Classifier, Research report RR-9510, Laboratoire de l'Informatique du Parallélisme, France.
43. Suerdem, A. K. and Karabekir, K. (2010) '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, No. 3, pp. 199-216.
44. Tremolières, R. C. (1979) 'The percolation method for an efficient grouping of data', Pattern Recognition, Vol. 11, No. 4, pp. 255-262.
45. Wan, R., Chen, J., Wang, L. and Su, X. (2009) 'Grid-based clustering over an evolving data stream', Int. J. Data Mining, Modelling and Management, Vol. 1, No. 4, pp. 393-490.
46. Wayne, C. L. (1998) 'Topic detection & tracking (TDT), overview & perspective', Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, Virginia.
47. Yu, L., Gao, L., Wang, D. and Fu, S. (2010) 'Quantitative function for community structure detection', Int. J. Data Mining, Modelling and Management, Vol. 2, No. 4, pp. 351-368.