An Ant Clustering Method for a Dynamic Database

  • Ling Chen
  • Li Tu
  • Yixin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


We propose an adaptive ant colony data clustering algorithm for a dynamic database. The algorithm uses a digraph where the vertices represent the data to be clustered. The weight of the edge represents the acceptance rate between the two data connected by the edge. The pheromone on the edges is adaptively updated by the ants passing through it. Some edges with less pheromone are progressively removed under a list of thresholds in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that the algorithm can find clusters more quickly and with better quality than K-means and LF. In addition, when the database is changed, the algorithm can dynamically modify the clusters accordingly to maintain its accuracy.


Data Item Acceptance Rate Travel Salesman Problem Strong Connected Component Dynamic Database 


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  1. 1.
    Kaufman, L., Pierreux, A., Rousseuw, P., Derde, M.P., Detaecernier, M.R., Massart, D.L., Platbrood, G.: Clustering on a Microcomputer with an Application to the Classification of Coals. Analytica Chimica Acta 153, 257–260 (1983)CrossRefGoogle Scholar
  2. 2.
    Lawson, R.G., Jurs, P.C.: Cluster Analysis of Acrylates to Guide Sampling for Toxicity Testing. Journal of Chemical Information and Computer Science 30(1), 137–144 (1990)Google Scholar
  3. 3.
    Beckers, M.L.M., Melssen, W.J., Buydens, L.M.C.: A self-organizing feature map for clustering nucleic acids. Application to a data matrix containing A-DNA and B-DNA dinucleotides. Comput. Chem. 21, 377–390 (1997)CrossRefGoogle Scholar
  4. 4.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  5. 5.
    Bonabeau, E., Dorigo, M., Théraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. In: Santa Fe Institute in the Sciences of the Complexity. Oxford University Press, Oxford (1999)Google Scholar
  6. 6.
    Dorigo, M., Maniezzo, V., Colomi, A.: Ant system: Optimization by a colony of coorperating agents. IEEE Transactions on Systems, Man and Cybernetics-Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  8. 8.
    Stutzle, T., Hoos, H.: MAX-MIN Ant systems. Future Generation Computer Sytems 16, 889–914 (2000)CrossRefGoogle Scholar
  9. 9.
    Dorigo, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. BioSystems 43(2), 73–81 (1997)CrossRefGoogle Scholar
  10. 10.
    Chang, C.S., Tian, L., Wen, F.S.: A new approach to fault section in power systems using Ant System. Electric Power Systems Research 49(1), 63–70 (1999)CrossRefGoogle Scholar
  11. 11.
    Gambardella, L.M., Dorigo, M.: HAS-SOP: An Hybrid Ant System for the Sequential Ordering Problem. Tech. Rep. No. IDSIA 97-11, IDSIA, Lugano Switzerland (1997)Google Scholar
  12. 12.
    Kuntz, P., Layzell, P., Snyder, D.: A colony of ant-like agents for partitioning in VLSI technology. In: Husbands, P., Harvey, I. (eds.) Proceedings of the Fourth European Conference on Artificial Life, pp. 412–424. MIT Press, Cambridge (1997)Google Scholar
  13. 13.
    Kuntz, P., Snyder, D.: New results on ant-based heuristic for highlighting the organization of large graphs. In: Proceedings of the 1999 Congress or Evolutionary Computation, pp. 1451–1458. IEEE Press, Piscataway (1999)Google Scholar
  14. 14.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The Dynamic of Collective Sorting Robot-like Ants and Ant-like Robots. In: Meyer, J.A., Wilson, S.W. (eds.) SAB 1990-1st Conf. On Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  15. 15.
    Lumer, E., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animates, vol. 3, pp. 501–508. MIT Press/ Bradford Books, Cambridge (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Chen
    • 1
    • 2
  • Li Tu
    • 1
  • Yixin Chen
    • 3
  1. 1.Department of Computer ScienceYangzhou UniversityYangzhouChina
  2. 2.State Key Lab of Novel Software TechNanjing UniversityNanjingChina
  3. 3.Department of Computer Science and EngineeringWashington University in St. LouisSt. LouisUSA

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