A new approach of data clustering using a flock of agents

Evolutionary Computation

Volumn 15, Issue 3, 2007, Pages 345-367

  Picarougne, Fabien ^a   Azzag, Hanene ^b   Venturini, Gilles ^c   Guinot, Christiane ^c,d  

^a UNIVERSITÉ DE NANTES   (France)

^b UNIVERSITÉ PARIS 13   (France)

^c UNIVERSITÉ DE TOURS   (France)

^d CRIES   (France)

Author keywords

Clustering; Data visualization; Flock of agents; Swarm intelligence; Unsupervised; Visual data mining

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BIOLOGY; CLUSTER ANALYSIS; DATA BASE; HUMAN; METABOLISM; SKIN; SOYBEAN; STATISTICAL ANALYSIS; STATISTICAL MODEL; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; DATA INTERPRETATION, STATISTICAL; DATABASES; HUMANS; MODELS, BIOLOGICAL; MODELS, GENETIC; MODELS, STATISTICAL; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; SKIN; SOYBEANS;

EID: 35748936479     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/evco.2007.15.3.345     Document Type: Conference Paper

References (46)

1. Aoki, I. (1984). Internal dynamics of fish schools in relation to inter-fish distance. In Bulletin ofthejaponese Society of Scientific Fisheries, 50(5):751-758.
2. Azzag, H., Guinot, C., and Venturini, G. (2006a). Swarm Intelligence and Data Mining, volume 34 of Studies in Computational Intelligence, chapter 7 - Data and text mining with hierarchical clustering ants, pages 153-190. Springer-Verlag.
3. Azzag, H., Picarougne, F., Guinot, C., and Venturini, G. (2006b). VRMiner: A Tool for Multimedia Database Mining with Virtual Reality, pages 318-339. Processing and Managing Complex Data for Decision Support. Idea Group Publishing, Hershey, PA.
4. Becker, R. A. and Cleveland, W. S. (1987). Brushing scatterplots. Technometrics, 29(2):127-142.
5. Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York.
6. Breder, C. (1951). Studies in the structure of the fish school. Bulletin of the American Museum of Natural History, 98(7FF).
7. Breder, C. (1959). Studies in the social grouping of fishes. Bulletin of the American Museum of Natural History, 117(399ff).
8. Broadwell, P., Myers, R., and Schaufler, R. (1985). Plasm: A fish sample. In Siggraph Visual Proceedings, Los Alamos. Siggraph 85 Art Show, ACM Press.
9. Buja, A. and Asimov, D. (1986). Grand tour methods: an outline. In Proceedings of the Seventeenth Symposium on the interface of computer sciences and statistics on Computer science and statistics, pages 63-67, New York, NY. Elsevier North-Holland, Inc.
10. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., and Bonabeau, E. (2001). Self-Organization in Biological Systems. Princeton University Press, Princeton, NJ.
11. Cleveland, W. (1993). Visualizing data. Hobart Press.
12. Cook, D., Buja, A., Cabrera, J., and Hurley, C. (1995). Grand tour and projection pursuit. Journal of Computational and Graphical Statistics, 4(3):155-172.
13. Cucchiara, R. (1993). Analysis and comparison of different genetic models for the clustering problem in image analysis. In Albrecht, R., Reeves, C., and Steele, N., editors, International Conference on Artificial Neural Networks and Genetic Algorithms, pages 423-427, Innsbruck. Springer-Verlag.
14. Cui, X., Gao, J., and Potok, T. E. (2006). A flocking based algorithm for document clustering analysis. Journal of Systems Architecture, 52(8):505-515.
15. Davis, J. (1975). Socially induced flight reactions in pigeons. Animal Behaviour, 23:597-601.
16. Davis, J. (1980). The cordinated aerobatics of dunlin flocks. Animal Behaviour, 28:668-673.
17. de Castro, L. N. and Zuben, F. J. V. (2000). An evolutionary immune network for data clustering. In Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00), pages 84-89, Washington, DC. IEEE Computer Society.
18. Falkenauer, E. (1994). A new representation and operators for genetic algorithms applied to grouping problems. Evolutionary Computation, 2(2):123-144.
19. Goss, S. and Deneubourg, J.-L. (1991). Harvesting by a group of robots. In Varela, F. and Bourgine, P., editors, Proceedings of the First European Conference on Artificial Life, pages 195-204, Cambridge, MA. MIT Press.
20. Guinot, C., Malvy, D., Morizot, F., Tenenhaus, M., Latreille, J., Lopez, S., Le Fur, I., Dubertret, L., and Tschachler, E. (2002). Use of multiple correspondence analysis and cluster analysis to study healthy human facial skin: development of a classification tool. Archives of Dermatology.
21. Hall, L. O., Özyurt, I. B., and Bezdek, J. C. (1999). Clustering with a genetically optimized approach. IEEE Transactions on Evolutionary Computation, 3(2):103-112.
22. Handl, J., Knowles, J., and Dorigo, M. (2006). Ant-based clustering and topographic mapping. Artificial Life, 12(1):35-61.
23. Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data. Prentice Hall Advanced Reference Series.
24. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3):264-323.
25. Jones, D. and Beltrano, M. (1991). Solving partitioning problems with genetic algorithms. In Belew, R. and Booker, L., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 442-449. Morgan Kaufmann, San Mateo, CA.
26. Keim, D. and Kriegel, H.-P. (1996). Visualization techniques for mining large databases: a comparison. IEEE Transactions on Knowledge and Data Engineering, Special Issue on Data Mining, 8(6):923-938.
27. Kuntz, P., Layzell, P., and Snyers, D. (1997). A colony of ant-like agents for partitioning in VLSI technology. In Husbands, P. and Harvey, I., editors, Proceedings of the Fourth European Conference on Artificial Life, pages 417-424, Cambridge, MA. MIT Press.
28. Labroche, N., Monmarché, N., and Venturini, G. (2002). A new clustering algorithm based on the chemical recognition system of ants. In Harmelen, F., editor, Proceedings of the 15th European Conference on Artificial Intelligence, pages 345-349, Lyon, France. IOS Press.
29. Lumer, E. and Faieta, B. (1994). Diversity and adaptation in populations of clustering ants. In Cliff, D., Husbands, P., Meyer, J., and W., S., editors, Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pages 501-508, Cambridge, MA. MIT Press.
30. McQueen, J. (1967). Some methods of classification and analysis of multivariate observations. In Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281-293. Berkeley, University of California Press.
31. Monmarché, N., Slimane, M., and Venturini, G. (1999). On improving clustering in numerical databases with artificial ants. In Floreano, D., Nicoud, J., and Mondala, F., editors, 5th European Conference on Artificial Life (ECAL'99), Lecture Notes in Artificial Intelligence, volume 1674, pages 626-635, Swiss Federal Institute of Technology, Lausanne, Switzerland. Springer-Verlag.
32. Nasraoui, O., González, F. A., Cardona, C., Rojas, C., and Dasgupta, D. (2003). A scalable artificial immune system model for dynamic unsupervised learning. In Cantó-Paz, E., Foster, J. A., Deb, K., Davis, L., Roy, R., O'Reilly, U.-M., Beyer, H.G., Standish, R. K., Kendall, G., Wilson, S. W., Harman, M., Wegener, J., Dasgupta, D., Potter, M. A., Schultz, A. C., Dowsland, K. A., Jonoska, N., and Miller, J. F., editors, GECCO, volume 2723 of Lecture Notes in Computer Science, pages 219-230, Chicago, IL. Springer.
33. Nasraoui, O. and Leon, E. (2004). On fitness, niching strategies, and hybrid niche size estimation for discovering an unknown number of clusters in noisy data. In Poli, R., Cagnoni, S., Keijzer, M., Costa, E., Pereira, F., Raidi, G., Upton, S. C., Goldberg, D., Lipson, H., de Jong, E., Koza, J., Suzuki, H., Sawai, H., Parmee, I., Pelikan, M., Sastry, K., Thierens, D., Stolzmann, W., Lanzi, P. L., Wilson, S. W., O'Neill, M., Ryan, C., Yu, T., Miller, J. F., Garibay, I., Holifield, G., Wu, A. S., Riopka, T., Meysenburg, M. M., Wright, A. W., Richter, N., Moore, J. H., Ritchie, M. D., Davis, L., Roy, R., and Jakiela, M., editors, GECCO 2004 Workshop Proceedings, Seattle, WA.
34. Parsons, L., Haque, E., and Liu, H. (2004). Subspace clustering for high dimensional data: a review. SIGKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining, 6(1):90-105.
35. Proctor, G. and Winter, C. (1998). Information flocking: Data visualisation in virtual worlds using emergent behaviours. In Heudin, J.-C., editor, Proceedings of the First International Conference on Virtual Worlds, volume 1434, pages 168-176, London, UK. Springer-Verlag.
36. Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics (SIGGRAPH '87 Conference Proceedings), 21(4):25-34.
37. Roweis, S. T. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326.
38. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27:379-423.
39. Swayne, D. F., Temple Lang, D., Buja, A., and Cook, D. (2003). GGobi: evolving from XGobi into an extensible framework for interactive data visualization. Computational Statistics & Data Analysis, 43:423-444.
40. Tenenbaum, J. B., de Silva, V., and Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323.
41. Timmis, J., Neal, M., and Hunt, J. (2000). An artificial immune system for data analysis. Biosystems, 55(1/3):143-150.
42. Torgerson, W. S. (1952). Multidimensional scaling: I. theory and method. Psychometrika, 17(4):401-419.
43. von Laszewski, G. (1991). Intelligent structural operators for the k-way graph partitioning problem. In Belew, R. and Booker, L., editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 45-52. Morgan Kaufmann, San Mateo, CA.
44. Wegman, E. J. and Symanzik, J. (2002). Immersive Projection Technology for Visual Data Mining. Journal of Computational and Graphical Statistics, 11(1):163-188.
45. Wong, P. C. and Bergeron, R. D. (1997). 30 years of multidimensional multivariate visualization. In Scientific Visualization, Overviews, Methodologies, and Techniques, pages 3-33, Washington, DC. IEEE Computer Society.
46. Young, F. (1985). Multidimensional scaling. Kotz & Johnson "Encyclopedia of Statistical Sciences", 5:649-658.