Advertisement

SACOC:A Spectral-Based ACO Clustering Algorithm

  • Héctor D. Menéndez
  • Fernando E. B. Otero
  • David Camacho
Part of the Studies in Computational Intelligence book series (SCI, volume 570)

Abstract

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, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest—an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experimentsmeasure the accuracy of the algorithm for both synthetic datasets and realworld datasets extracted from the UCI Machine Learning Repository.

Keywords

Clustering Data Mining ACO Spectral 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ashok, L., Messinger, D.W.: A spectral image clustering algorithm based on ant colony optimization, pp. 83,901P–83,901P–10 (2012)Google Scholar
  2. 2.
    Bache, K., Lichman, M.: UCI machine learning repository (2013), http://archive.ics.uci.edu/ml
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Larose, D.T.: Discovering Knowledge in Data. John Wiley & Sons (2005)Google Scholar
  8. 8.
    Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)CrossRefMathSciNetGoogle Scholar
  9. 9.
    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
  10. 10.
    Martens, D., Baesens, B., Fawcett, T.: Editorial survey: swarm intelligence for data mining. Machine Learning 82(1), 1–42 (2011)CrossRefMathSciNetGoogle Scholar
  11. 11.
    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
  12. 12.
    Ng, A., Jordan, M., Weiss, Y.: On Spectral Clustering: Analysis and an algorithm. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, pp. 849–856. MIT Press (2001)Google Scholar
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Shelokar, P., Jayaraman, V.K., Kulkarni, B.D.: An ant colony approach for clustering. Analytica Chimica Acta 509(2), 187–195 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

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 KentCanterburyUK

Personalised recommendations