A New Space Defined by Ant Colony Algorithm to Partition Data

  • Hamid Parvin
  • Behrouz Minaei-Bidgoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)


To reach a robust partition, ensemble-based learning is always a very promising option. There is straightforward way to generate a set of primary partitions that are different from each other, and then to aggregate the partitions via a consensus function to generate the final partition. Another alternative in the ensemble learning is to turn to fusion of different data from originally different sources. In this paper we introduce a new ensemble learning based on the Ant Colony clustering algorithm. Experimental results on some real-world datasets are presented to demonstrate the effectiveness of the proposed method in generating the final partition.


Ant Colony Data Fusion Clustering 


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  1. 1.
    Alizadeh, H., Minaei-Bidgoli, B., Parvin, H., Mohsen, M.: An Asymmetric Criterion for Cluster Validation. SCI, vol. 363. Springer, Heidelberg (2011)Google Scholar
  2. 2.
    Newman, C.B.D.J., Hettich, S., Merz, C.: UCI repository of machine learning databases (1998),
  3. 3.
    Strehl, A., Ghosh, J.: Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research 3, 583–617 (2002)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Kennedy, J., Russell, S.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  5. 5.
    Azimi, J., Cull, P., Fern, X.: Clustering ensembles using ants algorithm. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds.) IWINAC 2009. LNCS, vol. 5601, pp. 295–304. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Tsang, C.H., Kwong, S.: Ant Colony Clustering and Feature Extraction for Anomaly Intrusion Detection. SCI, vol. 34, pp. 101–123 (2006)Google Scholar
  7. 7.
    Liu, B., Pan, J., McKay, R.I.: Incremental clustering based on swarm intelligence. In: Wang, T.-D., Li, X., Chen, S.-H., Wang, X., Abbass, H.A., Iba, H., Chen, G.-L., Yao, X. (eds.) SEAL 2006. LNCS, vol. 4247, pp. 189–196. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: International Conference on Simulation of Adaptive Behavior: from animals to animates, pp. 356–363. MIT Press, Cambridge (1991)Google Scholar
  9. 9.
    Lumer, E.D., Faieta, B.: Diversity and adaptation in populations of clustering ants. In: International conference on simulation of adaptive behavior: from animals to animates, pp. 501–508. MIT Press, Cambridge (1994)Google Scholar
  10. 10.
    Munkres, J.: Algorithms for the Assignment and Transportation Problems. Journal of the Society for Industrial and Applied Mathematics 5(1), 32–38 (1957)MathSciNetCrossRefzbMATHGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hamid Parvin
    • 1
  • Behrouz Minaei-Bidgoli
    • 1
  1. 1.School of Computer EngineeringIran University of Science and Technology (IUST)TehranIran

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