MACOC: A Medoid-Based ACO Clustering Algorithm

  • Héctor D. Menéndez
  • Fernando E. B. Otero
  • David Camacho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8667)


The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, showing great potential of ACO-based techniques. This work presents an ACO-based clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach restructures ACOC from a centroid-based technique to a medoid-based technique, where the properties of the search space are not necessarily known. Instead, it only relies on the information about the distances amongst data. The new algorithm, called MACOC, has been compared against well-known algorithms (K-means and Partition Around Medoids) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashok, L., Messinger, D.W.: A spectral image clustering algorithm based on ant colony optimization 8390, 83901P–83901P-10 (2012),
  2. 2.
    Bache, K., Lichman, M.: UCI machine learning repository (2013),
  3. 3.
    Blum, C., Socha, K.: Training feed-forward neural networks with ant colony optimization: An application to pattern classification. In: Proceedings of HIS 2005, pp. 233–238. IEEE Computer Society, Washington, DC (2005), Google Scholar
  4. 4.
    Borrotti, M., Poli, I.: Naïve bayes ant colony optimization for experimental design. In: Kruse, R., Berthold, M., Moewes, C., Gil, M.A., Grzegorzewski, P., Hryniewicz, O. (eds.) Synergies of Soft Computing and Statistics. AISC, vol. 190, pp. 489–497. Springer, Heidelberg (2013), CrossRefGoogle Scholar
  5. 5.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977), zbMATHMathSciNetGoogle Scholar
  6. 6.
    Ding, S.: Feature selection based f-score and aco algorithm in support vector machine. In: Second International Symposium on Knowledge Acquisition and Modeling, KAM 2009, vol. 1, pp. 19–23 (2009)Google Scholar
  7. 7.
    de França, F.O., Coelho, G.P., Von Zuben, F.J.: bicACO: An Ant Colony Inspired Biclustering Algorithm. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 401–402. Springer, Heidelberg (2008), CrossRefGoogle Scholar
  8. 8.
    Hruschka, E., Campello, R., Freitas, A., de Carvalho, A.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(2), 133–155 (2009)CrossRefGoogle Scholar
  9. 9.
    Jafar, O.M., Sivakumar, R.: Ant-based clustering algorithms: A brief survey. International Journal of Computer Theory and Engineering 2, 787–796 (2010)CrossRefGoogle Scholar
  10. 10.
    Kao, Y., Cheng, K.: An aco-based clustering algorithm. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 340–347. Springer, Heidelberg (2006), CrossRefGoogle Scholar
  11. 11.
    Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. Reports of the Faculty of Mathematics and Informatics (1987),
  12. 12.
    Larose, D.T.: Discovering Knowledge in Data. John Wiley & Sons (2005)Google Scholar
  13. 13.
    Macqueen, J.B.: Some methods of classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  14. 14.
    Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Machine Learning 82(1), 1–42 (2011)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Menéndez, H.D., Barrero, D.F., Camacho, D.: A genetic graph-based approach for partitional clustering. Int. J. Neural Syst. 24(3) (2014)Google Scholar
  16. 16.
    Orgaz, G.B., Menéndez, H.D., Camacho, D.: Adaptive k-means algorithm for overlapped graph clustering. Int. J. Neural Syst. 22(5) (2012)Google Scholar
  17. 17.
    Otero, F., Freitas, A., Johnson, C.: Inducing decision trees with an ant colony optimization algorithm. Applied Soft Computing 12(11), 3615–3626 (2012)CrossRefGoogle Scholar
  18. 18.
    Otero, F., Freitas, A., Johnson, C.: A New Sequential Covering Strategy for Inducing Classification Rules With Ant Colony Algorithms. IEEE Transactions on Evolutionary Computation 17(1), 64–76 (2013)CrossRefGoogle Scholar
  19. 19.
    Parpinelli, R., Lopes, H., Freitas, A.: Data mining with an ant colony optimization algorithm. IEEE Trans. on Evolutionary Computation 6(4), 321–332 (2002)CrossRefGoogle Scholar
  20. 20.
    Schaeffer, S.: Graph clustering. Computer Science Review 1(1), 27–64 (2007)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics 1(6), 80–83 (1945)CrossRefGoogle Scholar
  22. 22.
    Zhang, X., Chen, X., He, Z.: An aco-based algorithm for parameter optimization of support vector machines. Expert Syst. Appl. 37(9), 6618–6628 (2010), CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Héctor D. Menéndez
    • 1
  • Fernando E. B. Otero
    • 2
  • David Camacho
    • 1
  1. 1.Departamento de Ingeniería InformáticaUniversidad Autónoma de MadridMadridSpain
  2. 2.School of ComputingUniversity of KentChatham MaritimeUnited Kingdom

Personalised recommendations