Incremental Clustering Based on Swarm Intelligence

  • Bo Liu
  • Jiuhui Pan
  • R I (Bob) McKay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


We propose methods for incrementally constructing a knowledge model for a dynamically changing database, using a swarm of special agents (ie an ant colony) and imitating their natural cluster-forming behavior. We use information-theoretic metrics to overcome some inherent problems of ant-based clustering, obtaining faster and more accurate results. Entropy governs the pick-up and drop behaviors, while movement is guided by pheromones. The primary benefits are fast clustering, and a reduced parameter set. We compared the method both with static clustering (repeatedly applied), and with the previous dynamic approaches of other authors. It generated clusters of similar quality to the static method, at significantly reduced computational cost, so that it can be used in dynamic situations where the static method is infeasible. It gave better results than previous dynamic approaches, with a much-reduced tuning parameter set. It is simple to use, and applicable to continuously- and batch-updated databases.


Static Cluster Swarm Intelligence Initial Cluster Cluster Quality Dynamic Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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., Detrain, C., Chretien, L.: The Dynamics of Collective Sorting: Robot-Like Ant and Ant-Like Robot. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings First Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  3. 3.
    Lumer, E., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: Proceedings Third International Conference on Simulation of Adaptive Behavior: From Animal to Animats, vol. 3, pp. 499–508. MIT Press, Cambridge (1994)Google Scholar
  4. 4.
    Ramos, V., Abraham, A.: Swarms on Continuous Data. In: Proceedings of 2003 IEEE Congress on Evolutionary Computation, pp. 1370–1375 (2003)Google Scholar
  5. 5.
    Barbar, D., Couto, J., Li, Y.: COOLCAT: An Entropy-based Algorithm for Categorical Clustering. In: Proceedings of the Eleventh International Conference on Information and Knowledge management, pp. 582–589 (2002)Google Scholar
  6. 6.
    Ramos, V., Merelo, J.J.: Self-organized Stigmergic Document Maps: Environment as A Mechanism for Context Learning. In: Alba, E., Herrera, F., Merelo, J.J., et al. (eds.) Procs.of AEB 2002-1st Spanish Conference on Evolutionary and Bio-inspired Algorithms, Spain, pp. 284–293 (2002)Google Scholar
  7. 7.
    Hettich, S., Bay, S.D.: The UCI KDD Archive [DB/OL] (1999),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bo Liu
    • 1
  • Jiuhui Pan
    • 1
  • R I (Bob) McKay
    • 2
  1. 1.Department of Computer ScienceJinan UniversityGuangzhouChina
  2. 2.Dept of Computer Science and EngineeringSeoul National UniversitySeoulKorea

Personalised recommendations