Abstract
We propose in this paper a new approach for topological hierarchical tree clustering inspired from self-assembly behavior of artificial ants. Our method called THT (Topologial Hierarchical Tree) builds, autonomously and simultaneously, a topological and hierarchical partitioning of data. Each ”cluster” associated to one cell of a 2D grid is modeled by a tree. The artificial ants that we define dissimilarly build a tree where each ant represents a node/data. The benefit of this novel approach is the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data unfold in highly intuitive fashion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31(3), 264–323 (1999)
Goss, S., Deneubourg, J.-L.: Harvesting by a group of robots. In: Varela, F., Bourgine, P. (eds.) Proceedings of the First European Conference on Artificial Life, pp. 195–204. Elsevier Publishing, Amsterdam (1991)
Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Cliff, D., Husbands, P., Meyer, J.A. (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pp. 501–508. MIT Press, Cambridge (1994)
Deneubourg, J.-L., Goss, S., Franks, N.R., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting: robot-like ant and ant-like robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pp. 356–365 (1990)
Kuntz, P., Snyers, D., Layzell, P.J.: A stochastic heuristic for visualising graph clusters in a bi-dimensional space prior to partitioning. J. Heuristics 5(3), 327–351 (1998)
Abraham, A., Ramos, V.: Web usage mining using artificial ant colony clustering and linear genetic programming. In: The Congress on Evolutionary Computation, Canberra, Australia, December 08-12, pp. 1384–1391. IEEE Press, Los Alamitos (2003)
Handl, J., Knowles, J., Dorigo, M.: On the performance of ant-based clustering, pp. 204–213 (2003)
Azzag, H., Guinot, C., Oliver, A., Venturini, G.: A hierarchical ant based clustering algorithm and its use in three real-world applications. In: Dullaert, K.S.W., Sevaux, M., Springael, J. (eds.) European Journal of Operational Research, EJOR (2006) Special Issue on Applications of Metaheuristics
Theraulaz, G., Bonabeau, E., Sauwens, C., Deneubourg, J.-L., Lioni, A., Libert, F., Passera, L., Sol, R.-V.: Model of droplet formation and dynamics in the argentine ant (linepithema humile mayr). Bulletin of Mathematical Biology (2001)
Kohonen, T.: Self-organizing Maps. Springer, Berlin (2001)
Blake, C.L., Merz, C.L.: UCI repository of machine learning databases. Technical report, University of California, Department of information and Computer science, Irvine, CA (1998) , http://ftp.ics.uci.edu/pub/machine-learning-databases
Shneiderman, B.: Tree visualization with tree-maps: A 2-D space-filling approach. ACM Transactions on Graphics 11, 92–99 (1992)
Saporta, G., Youness, G.: Concordance entre deux partitions: quelques propositions et expériences. In: Actes des 8es rencontres de la SFC, Pointeá Pitre (2001)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Recognition and Machine Intelligence 1(2), 224–227 (1979)
Wu, L.H., Hsu, P.Y.: The perceptual eye view: A user-defined method for information visualization, Human-Computer Interaction. Interaction Platforms and Techniques, pp. 181–190
Mendis, B.S.U., Gedeon, T.D., Botzheim, J., Kóczy, L.T.: Generalised Weighted Relevance Aggregation Operators for Hierarchical Fuzzy Signatures. In: International Conference on Computational Inteligence for Modelling Control and Automation (CIMCA 2006), Sydney, Australia (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lebbah, M., Azzag, H. (2010). Topological Hierarchical Tree Using Artificial Ants. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-17537-4_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
eBook Packages: Computer ScienceComputer Science (R0)