Abstract
AntClust is a clustering algorithm that is inspired by the chemical recognition system of real ants. It associates the genome of each artificial ant to an object of the initial data set and simulates meetings between ants to create nests of individuals that share a similar genome. Thus, the nests realize a partition of the original data set with no hypothesis concerning the output clusters (number, shape, size ...) and with minimum input parameters. Due to an internal mechanism of nest selection and finalization, AntClust runs in the worst case in quadratic time complexity with the number of ants. In this paper, we evaluate new heuristics for nest selection and finalization that allows AntClust to run on linear time complexity with the number of ants.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bonabeau, E., Dorigo, M., Theraulaz, G.: From natural to artificial swarm intelligence. Oxford University Press, New York (1999)
Semet, Y., Lutton, E., Collet, P.: Ant colony optimisation for e-learning: Observing the emergence of pedagogic suggestions. In: IEEE Swarm Intelligence Symposium 2003, Indianapolis, Indiana (April 2003)
Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Cliff, D., Husbands, P., Meyer, J., S.W., (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pp. 501–508. MIT Press, Cambridge (1994)
Monmarché, N., Slimane, M., Venturini, G.: In: Floreano, D., Nicoud, J., Mondala, F. (eds.) On improving clustering in numerical databases with artificial ants, Swiss Federal Institute of Technology, Lausanne, Switzerland, September 13-17. LNCS (LNAI), pp. 26–635. Springer, Heidelberg (1999)
Handl, J., Meyer, B.: Improved ant-based clustering and sorting in a document retrieval interface. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, p. 913. Springer, Heidelberg (2002)
Ramos, V., Merelo, J.: Self-organized stigmergic document maps: Environment as mechanism for context learning. In: Alba, E., Herrera, F., Merelo, J.J., et al. (eds.) Proceedings of the First Spanish Conference on Evolutionary and Bio-Inspired Algorithms (AEB 2002), Centro Univ. de Mérida, Mérida, Spain, February 6-8 (2002)
Labroche, N., Monmarché, N., Venturini, G.: Antclust: Ant clustering and web usage mining. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, Springer, Heidelberg (2003)
Hölldobler, B., Wilson, E.: Colony odor and kin recognition. In: The Ants, pp. 197–208. Springer, Berlin (1990)
Heer, J., Chi, E.: Mining the structure of user activity using cluster stability. In: Proceedings of the Workshop on Web Analytics, SIAM Conference on Data Mining (April 2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Labroche, N., Guinot, C., Venturini, G. (2004). Fast Unsupervised Clustering with Artificial Ants. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_115
Download citation
DOI: https://doi.org/10.1007/978-3-540-30217-9_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23092-2
Online ISBN: 978-3-540-30217-9
eBook Packages: Springer Book Archive