Self Organized Biogeography Algorithm for Clustering

  • Leila Hamdad
  • Anissa Achab
  • Amira Boutouchent
  • Fodil Dahamni
  • Karima Benatchba
Conference paper

DOI: 10.1007/978-3-642-38637-4_41

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)
Cite this paper as:
Hamdad L., Achab A., Boutouchent A., Dahamni F., Benatchba K. (2013) Self Organized Biogeography Algorithm for Clustering. In: Ferrández Vicente J.M., Álvarez Sánchez J.R., de la Paz López F., Toledo Moreo F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg

Abstract

We propose in this work a new self organized biomimitic approach for unsupervised classification, named BFC, based on BBO (Biogeography based optimization). This method is tested on several real datasets(IRIS, Satimages and heart). These benchmarks are characterized by increasing overlap degree. Moreover, a comparison of BFC with other clustering methods having proven their efficiency is presented. We will highlight the impact of this overlap on the performance of the methods.

Keywords

Clustering Self organization Biomimetic method Biogeography Biogeography based optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Leila Hamdad
    • 1
  • Anissa Achab
    • 2
  • Amira Boutouchent
    • 2
  • Fodil Dahamni
    • 2
  • Karima Benatchba
    • 2
  1. 1.LCSIEcole Nationale Supérieure d’InformatiqueAlgerAlgrie
  2. 2.LMCSEcole Nationale Supérieure d’InformatiqueAlgerAlgrie

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