Correlations Involved in a Bio-inspired Classification Technique

  • Camelia-M. Pintea
  • Sorin V. Sabau
Part of the Studies in Computational Intelligence book series (SCI, volume 387)

Abstract

An improved unsupervised bio-inspired clustering model is introduced. The main goal is to involve a correlation between properties of objects and some bio-inspired factors. The statistical classification biological model is based on the chemical recognition system of ants. Ants are able to create groups discriminating between nest-mates and intruders based on similar odor. Comparative analysis are performed on real data sets.

Keywords

Acceptance Threshold Expected Utility Function AntClust Algorithm Rapport Interne Chemical Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Blake, C.L., Merz, C.J.: Machine learning repository (1998)Google Scholar
  2. 2.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Proceedings of the First European Conference on Artificial Life, pp. 134–142. Elsevier Publishing, Amsterdam (1991)Google Scholar
  3. 3.
    Golden, B.L., Assad, A.A.: A decision-theoretic framework for comparing heuristics. European J. of Oper. Res. 18, 167–171 (1984)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Holldobler, B., Wilson, E.O.: The Ants. Springer, Heidelberg (1990)Google Scholar
  5. 5.
    Jain, A.K., Dubes, R.C.: Square-Error Clustering method. In: Algorithms for clustering data. Prentice Hall Advanced References series, pp. 96–101 (1988)Google Scholar
  6. 6.
    Kuntz, P., Snyers, D.: Emergent Colonization and Graph Partitioning. In: Proceedings of the Third Int. Conf. on Simulation of Adaptive Behaviour, pp. 494–500. MIT Press, Cambridge (1994)Google Scholar
  7. 7.
    Labroche, N., Monmarché, N., Venturini, G.: A new clustering algorithm based on the chemical recognition system of ants. In: Proceedings of the European Conference of Artificial Intelligence, pp. 345–349. IOS Press, Amsterdam (2002)Google Scholar
  8. 8.
    Labroche, N., Monmarché, N., Lenoir, A., Venturini, G.: Modélisation du système de reconnaissance chimique des fourmis. Rapport interne, Lab. d’Informatique de l’Universite du Tours (2002)Google Scholar
  9. 9.
    Labroche, N., Monmarché, N., Venturini, G.: Web session clustering with artificial ants colonies. In: Proceedings of Conference WWW 2003, Budapest, Hungary (2003)Google Scholar
  10. 10.
    Monmarché, N.: Algorithmes de fourmis artificielles: applications à la classification et à l’optimisation. Thèse de doctorat, Lab. d’Informatique de l’Universite de Tours (2000)Google Scholar
  11. 11.
    Monmarché, N., Slimane, M., Venturini, G.: On Improving Clustering in Numerical Databases with Artificial Ants. LNCS (LNAI), pp. 626–635 (1999)Google Scholar
  12. 12.
    Pintea, C.-M.: A clustering model based on ant-system. In: Proceeding of Int. Conf. of Applied Mathematics (ICAM5), p. 38 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Camelia-M. Pintea
    • 1
  • Sorin V. Sabau
    • 2
  1. 1.G.Cosbuc N.CollegeCluj-NapocaRomania
  2. 2.Tokai UniversitySapporoJapan

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