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Strengths and Weaknesses of Ant Colony Clustering

  • Lutz HerrmannEmail author
  • Alfred Ultsch
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Ant colony clustering (ACC) is a promising nature-inspired technique where stochastic agents perform the task of clustering high-dimensional data on a low-dimensional output space. Most ACC methods are derivatives of the approach proposed by Lumer and Faieta. These methods usually perform poorly in terms of topographic mapping and cluster formation. In particular when compared to clustering on Emergent Self-Organizing Maps (ESOM). In order to address this issue, a unifying representation for ACC methods and Emergent Self-Organizing Maps is derived in a brief yet formal manner. ACC terms are related to corresponding mechanisms of the Self-Organizing Map. This leads to insights on both algorithms. ACC are considered as first-degree relatives of the ESOM. This explains benefits and shortcomings of ACC and ESOM. Furthermore, the proposed unification allows to judge whether modifications improve an algorithm’s clustering abilities or not. This is demonstrated using a set of cardinal clustering problems.

Keywords

Clustering Emergent self-organizing maps Swarm intelligence 

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References

  1. Aranha, C., & Iba, H. (2006). The effect of using evolutionary algorithms on ant clustering techniques. In The Long Pham, Hai Khoi Le, & Xuan Hoai Nguyen (Eds.), Proceedings of the Third Asian-Pacific workshop on Genetic Programming (pp. 24–34). Military Technical Academy, Hanoi, VietNam.Google Scholar
  2. Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. In Annals of eugenics, 7, Part II (pp. 179–188). Cambridge: Cambridge University Press.Google Scholar
  3. Fundamental Clustering Problem Suite. (n.d.). http://www.uni-marburg.de/fb12/datenbionik/data.
  4. Goodhill, G. J., & Sejnowski, T. J. (1996). Quantifying neighbourhood preservation in topographic mappings, In Proceedings of 3rd Joint Symposium on Neural Computation. California Institute of Technology.Google Scholar
  5. Handl, J., Knowles, J., & Dorigo, M. (2006). Ant-based clustering and topographic mapping. Artificial Life, 12(1), 35–61. Cambridge, MA: MIT Press.Google Scholar
  6. Kohonen, T. (1995). Self-organizing maps. Springer Series in Information Sciences (Vol. 30). Berlin Heidelberg New York: Springer.Google Scholar
  7. Lumer, E., & Faieta, B. (1994). Diversity and adaption 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). Cambridge, MA: MIT Press.Google Scholar
  8. Nybo, K., Venna, J., & Kaski, S. (2007). The self-organizing map as a visual neighbor retrieval method. In Proceedings of the Sixth International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld.Google Scholar
  9. Tan, S. C., Ting, K. M., & Teng, S. W. (2006). Reproducing the results of ant-based clustering without using ants. In IEEE Congress on Evolutionary Computation.Google Scholar
  10. Ultsch, A., & Herrmann, L. (2006). Automatic Clustering with U*C (Technical report). Department of Mathematics and Computer Science, Philipps-University of Marburg.Google Scholar
  11. Ultsch, A., & Mörchen, F. (2006). U-maps: Topograpic visualization techniques for projections of high dimensional data. In Proceedings of 29th Annual Conference of the German Classification Society (GfKl 2006). Berlin.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Databionics Research Group, Department of Mathematics and Computer SciencePhilipps University of MarburgMarburgGermany

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