Strategies for the Increased Robustness of Ant-Based Clustering

  • Julia Handl
  • Joshua Knowles
  • Marco Dorigo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2977)


This paper introduces a set of algorithmic modifications that improve the partitioning results obtained with ant-based clustering. Moreover, general parameter settings and a self-adaptation scheme are devised, which afford the algorithm’s robust performance across varying data sets. We study the sensitivity of the resulting algorithm with respect to two distinct, and generally important, features of data sets: (i) unequal-sized clusters and (ii) overlapping clusters. Results are compared to those obtained using k-means, one-dimensional self-organising maps, and average-link agglomerative clustering. The impressive capacity of ant-based clustering to automatically identify the number of clusters in the data is additionally underlined by comparing its performance to that of the Gap statistic.


Grid Cell Data Item Agglomerative Cluster Neighbourhood Function Picking Operation 
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  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence – From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  2. 2.
    Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting: Robot-like ants and ant-like robots. In: Meyer, J.-A., Wilson, S. (eds.) Proceedings of the First International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 1, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  3. 3.
    Handl, J.: Ant-based methods for tasks of clustering and topographic mapping: extensions, analysis and comparison with alternative methods. Masters thesis. Chair of Artificial Intelligence, University of Erlangen-Nuremberg, Germany (November 2003),
  4. 4.
    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, pp. 913–923. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)Google Scholar
  6. 6.
    Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Proceedings of the Third International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 3, pp. 501–508. MIT Press, Cambridge (1994)Google Scholar
  7. 7.
    MacQueen, L.: Some methods for classification and analysis of multivariate observations. In: LeCam, L., Neyman, J. (eds.) Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)Google Scholar
  8. 8.
    Milligan, G.W.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179 (1985)CrossRefGoogle Scholar
  9. 9.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a dataset via the Gap statistic. Technical Report 208, Department of Statistics, Stanford University (2000),
  10. 10.
    van Rijsbergen, C.: Information Retrieval, 2nd edn. Butterworths, London (1979)Google Scholar
  11. 11.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parkankangas, J.: SOM Toolbox for Matlab 5. Technical Report A57, Neural Networks Research Centre, Helsinki University of Technology, Espoo, Finland (April 2000)Google Scholar
  12. 12.
    Vorhees, E.: The effectiveness and efficiency of agglomerative hierarchical clustering in document retrieval. PhD thesis, Department of Computer Science, Cornell University, UK (1985)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Julia Handl
    • 1
  • Joshua Knowles
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIAUniversité Libre de Bruxelles 

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