Sparrow: A Spatial Clustering Algorithm using Swarm Intelligence

IASTED International Multi-Conference on Applied Informatics

Volumn 21, Issue , 2003, Pages 50-55

  Folino, Gianluigi ^a   Spezzano, Giandomenico ^a  

^a ICAR CNR   (Italy)

Author keywords

Artificial life; Data mining; Spatial clustering; Swarm intelligence

Indexed keywords

ALGORITHMS; BIOMEDICAL EQUIPMENT; COMPUTER SIMULATION; DATA MINING; DATABASE SYSTEMS; GEOGRAPHIC INFORMATION SYSTEMS; ITERATIVE METHODS; MATHEMATICAL MODELS; PROBLEM SOLVING; VIDEO CAMERAS;

ARTIFICIAL LIFE; SPATIAL CLUSTERING; SWARM INTELLIGENCE;

INTELLIGENT NETWORKS;

EID: 1442326937     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Han J., Kamber M., Data Mining: Concepts and Techniques, Morgan Kaufmann 2000.
2. Kaufman L., Rousseeuw P. J., Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, 1990.
3. Karypis G., Han E., Kumar V.,: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling, IEEE Computer, vol. 32, pp.68-75, 1999.
4. Sander J., Ester M., Kriegel H.-P., Xu X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, in: Data Mining and Knowledge Discovery, an Int. Journal, Kluwer Academic Publishers, Vol. 2, No. 2, 1998, pp. 169-194.
5. Wang W., Yang J., Muntz R., STING: A Statistical Information Grid Approach to Spatial Data Mining, Proc. of Int. Conf. Very Large Data Bases (VLDB'97), pp. 186-195, 1997.
6. Han J., Kamber M., Tung A.K.H., Spatial Clustering Methods in Data Mining: A Survey, H. Miller and J. Han (eds.), Geographic Data Mining and Knowledge Discovery, Taylor and Francis, 2001.
7. Deneubourg J. L., Goss S., Franks, N., Sendova-Franks A., Detrain C., and Chretien L., The Dynamic of Collective Sorting Robot-like Ants and Ant-like Robots, Proc. of the first Conf. on Simulation of Adaptive Behavior, J.A. Meyer et S.W. Wilson (Eds), MIT Press/Bradford Books, pp. 356-363, 1990.
8. Lumer E. D., Faieta B., Diversity and Adaptation in Populations of Clustering Ants, Proc. of the third Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animals (SAB94), D. Cliff, P. Husbands, J.A. Meyer, S.W. Wilson (Eds), MIT-Press, pp. 501-508, 1994.
9. Kuntz P. Snyers D., Emergent Colonization and Graph Partitioning, Proc. of the third Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animats (SAB94), D. Cliff, P. Husbands, J.A. Meyer, S.W. Wilson (Eds), MIT-Press, pp. 494-500, 1994.
10. N. Monmarché, M. Slimane, and G. Venturini, "On improving clustering in numerical databases with artificial ants", in Advances in Artificial Life: 5th European Conference, ECAL 99, LNCS 1674, Springer, Berlin, pp. 626-635, 1999.
11. Bonabeau E., Dorigo M., Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, 1999.
12. Grassé, P.P., "La Reconstruction du nid et les Coordinations Inter-Individuelles chez Beellicositermes Natalensis et Cubitermes sp. La Théorie de la Stigmergie: Essai d'interprétation du Comportement des Termites Constructeurs, Insect. Soc. 6 pp. 41-80, 1959.
13. Macgill, J., Openshaw, S., The use of Flocks to drive a Geographic Analysis Machine, in Proceedings of the 3rd International Conference on GeoComputation, University of Bristol, United Kingdom, 1998.
14. James Macgill, Using Flocks to Drive a Geographical Analysis Engine, Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life, MIT Press, Reed College, Portland, Oregon, pp. 1-6, 2000.
15. Reynolds C. W., Flocks, Herds, and Schools: A Distributed Behavioral Model, Computer Graphics vol. 21, n. 4, (SIGGRAPH 87), pp. 25-34, 1987.
16. V. S. Colella, E. Klopfer, M. Resnick, Adventures in Modeling: Exploring Complex, Dynamic Systems with Star Logo, Teachers. College Press, 2001.
17. Ester M., Kriegel H.-P., Sander J., Xu X., 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), Portland, OR, 1996, pp. 226-231, 1996.