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.
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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. https://doi.org/10.1007/978-3-642-38637-4_41
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DOI: https://doi.org/10.1007/978-3-642-38637-4_41
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