The Use of Strategies of Normalized Correlation in the Ant-Based Clustering Algorithm

  • Arkadiusz Lewicki
  • Krzysztof Pancerz
  • Ryszard Tadeusiewicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)

Abstract

The article presents a new approach to the evaluation process associated with the modification of the ant-based clustering algorithm. The main aim of this study is to determine the degree of impact of the proposed changes on the results of the implemented clustering algorithm, whose task is not only to obtain the lowest intra-group variance, but also to self-determine the amount of target classes. These modifications concern both a different way of choosing the radius of perception considering the neighborhood of objects in a search decision space, as well as a use of a completely different metric than the Euclidean one for calculating the dissimilarity of objects based on the components including the normalized angular correlation of objects under consideration.

Keywords

ATTA metaheuristics strategy heuristic decision-making system clustering data data mining unsupervised clustering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hore, P.: Distributed clustering for scaling classic algorithms, Theses and Dissertations, University of South Florida (2004)Google Scholar
  2. 2.
    Lewicki, A., Tadeusiewicz, R.: The recruitment and selection of staff problem with an Ant Colony System, Backgrounds and Applications. AISC, vol. 2. Springer, Heidelberg (2010)Google Scholar
  3. 3.
    Lewicki, A., Generalized non-extensive thermodynamics to the Ant Colony System, Information Systems Architecture and Technology, System Analysis Approach to the Design, Control and Decision Support, Wroclaw (2010) Google Scholar
  4. 4.
    Lewicki, A.: Non-Euclidean metric in multi-objective Ant Colony Optimization Algorithms, Information Systems Architecture and Technology, System Analysis Approach to the Design, Control and Decision Support, Wroclaw (2010)Google Scholar
  5. 5.
    Lewicki, A., Tadeusiewicz, R.: An autocatalytic emergence swarm algorithm in the decision-making task of managing the process of creation of intellectual capital. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artif. Life 12(1) (2006)Google Scholar
  7. 7.
    Decastro, L., Von Zuben, F.: Recent Developments In Biologically Inspired Computing. Idea Group Publishing, Hershey (2004)Google Scholar
  8. 8.
    Mohamed, O., Sivakumar, R.: Ant-based Clustering Algorithms: A Brief Survey. International Journal of Computer Theory and Engineering 2(5) (October 2010)Google Scholar
  9. 9.
    Dorigo, M., Di Caro, G., Gambarella, L.: Ant Algorithms for Discrete Optimization. Artificial Life 5(3) (1999)Google Scholar
  10. 10.
    Azzag, H., Monmarché, N., Slimane, M., Venturini, G., Guinot, C.: AntTree: A new model for clustering with artificial ants. In: IEEE Congress on Evolutionary Computation, vol. 4, pp. 2642–2647. IEEE Press, Canberra (2003)Google Scholar
  11. 11.
    Scholes, S., Wilson, M., Sendova-Franks, A., Melhuish, C.: Comparisons in evolution and engineering: The collective intelligence of sorting. Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems 12(3-4) (2004)Google Scholar
  12. 12.
    Sendova-Franks, A.: Brood sorting by ants: two phases and differential diffusion. Animal Behaviour (2004)Google Scholar
  13. 13.
    Boryczka, B.: Ant Clustering Algorithm, Intelligent Information Systems. Kluwer Academic Publishers (2008)Google Scholar
  14. 14.
    Abbass, H., Hoai, N., McKay, R.: AntTAG, A new method to compose computer using colonies of ants. In: Proceedings of the IEEE Congress on Evolutianory Computation, Honolulu, vol. 2 (2002)Google Scholar
  15. 15.
    Vizine, A., de Castro, L., Hruschka, E., Gudwin, R.: Towards improving clustering ants: An adaptive clustering algorithm. Informatica Journal 29 (2005)Google Scholar
  16. 16.
    Ouadfel, S., Batouche, M.: An Efficient Ant Algorithm for Swarm-based Image Clustering. Journal of Computer Science 3(3)Google Scholar
  17. 17.
    Deneubourg, J., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behavior: From Animals to Animats. MIT Press, Cambridge (1990)Google Scholar
  18. 18.
    Das, S., Abraham, A., Konar, A.: Metaheuristic Clustering. Springer, Heidelberg (2009)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Arkadiusz Lewicki
    • 1
  • Krzysztof Pancerz
    • 1
  • Ryszard Tadeusiewicz
    • 2
  1. 1.University of Information Technology and Management in RzeszówPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

Personalised recommendations