Data Mining with Ant Colony Algorithms

  • Ilaim Costa Junior
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)


The Ant-Miner algorithm, Ant-Miner2, Ant-Miner3 and Taco-Miner have an excellent performance in classification tasks, what can be seen in literature. These algorithms are inspired on the behavior of real ant colonies and some data mining concepts as well as principles. This paper presents a new algorithm based on Ant Colony whose experiments comparing with the others suggest superiority.


Rule discovery data mining computational intelligence ant colony algorithm multi-agent systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: New Ideas in Optimization, pp. 11–32. McGraw Hill, London (1999)Google Scholar
  2. 2.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  3. 3.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data Mining with an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computing 6(4) (2002)Google Scholar
  4. 4.
    Rozin, V., Margaliot, M.: The Fuzzy Ant. IEEE Computational Intelligence Magazine 2(4) (2007)Google Scholar
  5. 5.
    Parpinelli, R.S.: Um Algoritmo Baseado em Colônias de Formigas para Classificação e, Data Mining. Dissertação de Mestrado, UTFPR, Curitiba (2001) (in Portuguese)Google Scholar
  6. 6.
    Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from database perspective. Proceedings of the IEEE Transactions on Knowledge and Data Engineering, 866–883 (1996)Google Scholar
  7. 7.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
  8. 8.
    Clark, P., Neblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–283 (1989)Google Scholar
  9. 9.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, New York (1991)zbMATHCrossRefGoogle Scholar
  10. 10.
    Frank, A., Asuncion, A.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2010),
  11. 11.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
  12. 12.
    Liu, B., Abbass, H.A., Mckay, B.: Density-Based Heuristic for Rule Discovery with Ant- Miner. In: Australia-Japan Workshop on Intelligent and Evolutionary Systems (2002)Google Scholar
  13. 13.
    Liu, B., Abbass, H.A., Mckay, B.: Classification Rule Discovery with Ant Colony Optimization. In: IAT 2003, International Conference on Intelligent Agent Technology (2003)Google Scholar
  14. 14.
    Schools, L., Naudts, B.: Ant Colonies are Good at Solving Constraint Satisfaction Problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1190–1195 (2000)Google Scholar
  15. 15.
    Sun, R., Tatsumi, S., Zhao, G.: Multiagent Reinforcement Learning Method with An Improved Ant Colony Systems. In: Proceedings of the 2001 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 1612–1617 (2001)Google Scholar
  16. 16.
    Thangavel, K., Jaganathan, P.: Rule Minig Algorithm with a New Ant Colony Optimization Algorithm. In: IEEE International Conference on Computational Intelligence and Multimedia Applications (2007)Google Scholar
  17. 17.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery & Data Mining, pp. 1–34. MIT Press, Cambridge (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ilaim Costa Junior
    • 1
  1. 1.Institute of ComputingFluminense Federal UniversityNiteróiBrazil

Personalised recommendations