Advertisement

Ant Colony Inspired Clustering Based on the Distribution Function of the Similarity of Attributes

  • Arkadiusz LewickiEmail author
  • Krzysztof Pancerz
  • Ryszard Tadeusiewicz
Conference paper
  • 1.1k Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 457)

Abstract

The paper presents results of research on the clustering problem on the basis of swarm intelligence using a new algorithm based on the normalized cumulative distribution function of attributes. In this approach, we assume that the analysis of likelihood of the occurrence of particular types of attributes and their values allows us to measure the similarity of the objects within a given category and the dissimilarity of the objects between categories. Therefore, on the basis of the complex data set of attributes of any type, we can successfully raise a lot of interesting information about these attributes without necessity of considering their real meaning. Our research shows that the algorithm inspired by the mechanisms observed in nature may return better results due to the modification of the neighborhood based on the similarity coefficient.

Keywords

ant colony clustering analysis ant colony optimization swarm intelligence self-organization unsupervised clustering data mining distribution function 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbass, H., Hoai, N., McKay, R.: AntTAG: A new method to compose computer programs using colonies of ants. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu (2002)Google Scholar
  2. 2.
    Azzag, H., Monmarche, N., Slimane, M., Venturini, G.: AntTree: a new model for clustering with artificial ants. In: Proceedings of the 2003 Congress on Evolutionary Computation, Beijing, China, pp. 2642–2647 (2003)Google Scholar
  3. 3.
    Berkhin, P.: Survey of clustering data mining techniques. Tech. rep. Accrue Software, Inc., San Jose, California (2002)Google Scholar
  4. 4.
    Bin, W., Zhongzi, S.: A clustering algorithm based on swarm intelligence. In: Proceedings of 2001 International Conferences on Info-tech and Info-net, Beijing, China, pp. 58–66 (2001)Google Scholar
  5. 5.
    Boryczka, U.: Ant clustering algorithm. In: Proceedings of the Conference on Intelligent Information Systems, Zakopane, Poland, pp. 377–386 (2008)Google Scholar
  6. 6.
    Deneubourg, J., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting: Robot-like ants and ant-like robots. In: Proceedings of the First International Conference on Simulation of Adaptive Behaviour: From Animals to Animats 1, pp. 356–365. MIT Press, Cambridge (1991)Google Scholar
  7. 7.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)CrossRefGoogle Scholar
  8. 8.
    Dunn, J.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics 3(3), 32–57 (1973)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Han, Y., Shi, P.: An improved ant colony algorithm for fuzzy clustering in image segmentation. Neurocomputing 70(4-6), 665–671 (2007)CrossRefGoogle Scholar
  10. 10.
    Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artificial Life 12(1), 35–62 (2006)CrossRefGoogle Scholar
  11. 11.
    Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering: a comparative study of its relative performance with respect to k-means, average link and 1d-som. Tech. rep., IRIDIA (2003)Google Scholar
  12. 12.
    Handl, J., Knowles, J., Dorigo, M.: Strategies for the Increased Robustness of Ant-based Clustering. In: Di Marzo Serugendo, G., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.) ESOA 2003. LNCS (LNAI), vol. 2977, pp. 90–104. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Lewicki, A.: Generalized non-extensive thermodynamics to the ant colony system. In: Świa̧tek, J., Borzemski, L., Grzech, A., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: System Analysis Approach to the Design, Control and Decision Support, Wroclaw (2010)Google Scholar
  14. 14.
    Lewicki, A.: Non-euclidean metric in multi-objective ant colony optimization algorithms. In: Świa̧tek, J., Borzemski, L., Grzech, A., Wilimowska, Z. (eds.) Information Systems Architecture and Technology: System Analysis Approach to the Design, Control and Decision Support, Wroclaw (2010)Google Scholar
  15. 15.
    Lewicki, A., Tadeusiewicz, R.: The recruitment and selection of staff problem with an ant colony system. In: Proceedings of the 3rd International Conference on Human System Interaction, Rzeszów, Poland, pp. 770–774 (2010)Google Scholar
  16. 16.
    Lewicki, A., Tadeusiewicz, R.: An Autocatalytic Emergence Swarm Algorithm in the Decision-Making Task of Managing the Process of Creation of Intellectual Capital. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds.) Human – Computer Systems Interaction, Part I. AISC, vol. 98, pp. 271–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Lewicki, A., Pancerz, K., Tadeusiewicz, R.: The Use of Strategies of Normalized Correlation in the Ant-Based Clustering Algorithm. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011, Part I. LNCS, vol. 7076, pp. 637–644. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Ouadfel, S., Batouche, M.: An efficient ant algorithm for swarm-based image clustering. Journal of Computer Science 3(3), 162–167 (2007)CrossRefGoogle Scholar
  19. 19.
    Rand, W.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)CrossRefGoogle Scholar
  20. 20.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth, London (1979)Google Scholar
  21. 21.
    Scholes, S., Wilson, M., Sendova-Franks, A.B., Melhuish, C.: Comparisons in evolution and engineering: The collective intelligence of sorting. Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems 12(3-4), 147–159 (2004)Google Scholar
  22. 22.
    Vizine, A., de Castro, L., Hruschka, E., Gudwin, R.: Towards improving clustering ants: An adaptive ant clustering algorithm. Informatica 29(2), 143–154 (2005)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

Personalised recommendations