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Newcastle Disease Virus Clustering Based on Swarm Rapid Centroid Estimation

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Advances in Nature and Biologically Inspired Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 419))

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Abstract

Newcastle disease is considered to be one of the most important serious infectious poultry disease. The work introduced in this paper addresses the problem of clustering Newcastle disease dataset obtained from the National Center for Biotechnology Information GenBank (NCBI). A lightweight swarm clustering algorithm called Rapid Centroid Estimation (RCE) is applied in the clustering task. However, the best number of clusters is selected using silhouette measure. Hence, RCE is compared with K-means for the same number of clusters. The experiment shows that the external quality measures (purity, entropy, rand index, precision, recall and F-measure) of the RCE clustering technique outperform the ones of the K-means.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Member of Scientific Research Group in Egypt, Misr International University, Cairo, Egypt

    Fatma Helmy Ismail

  2. Faculty of Computers and Information, Member of Scientific Research Group in Egypt, Suez Canal University, Ismailia, Egypt

    Ahmed Fouad Ali

  3. Faculty of Veterinary Medicine, Member of Scientific Research Group in Egypt, Cairo University, Giza, Egypt

    Saleh Esmat

  4. Faculty of Computers and Information, Chair of Scientific Research Group in Egypt, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Authors
  1. Fatma Helmy Ismail
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  2. Ahmed Fouad Ali
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  3. Saleh Esmat
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  4. Aboul Ella Hassanien
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Editor information

Editors and Affiliations

  1. School of Mathematics, Statistics and CS, University of KwaZulu-Natal, Pietermaritzburg, South Africa

    Nelishia Pillay

  2. Sch of IT, Dept of CS, University of Pretoria, Pretoria, South Africa

    Andries P. Engelbrecht

  3. Sci N/w for Innova and Research Exc, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  4. Department of Computing Sciences,, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Mathys C. du Plessis

  5. Faculty of Electrical Engineering and, Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Václav Snášel

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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Ismail, F.H., Ali, A.F., Esmat, S., Hassanien, A.E. (2016). Newcastle Disease Virus Clustering Based on Swarm Rapid Centroid Estimation. In: Pillay, N., Engelbrecht, A., Abraham, A., du Plessis, M., Snášel, V., Muda, A. (eds) Advances in Nature and Biologically Inspired Computing. Advances in Intelligent Systems and Computing, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-319-27400-3_32

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  • DOI: https://doi.org/10.1007/978-3-319-27400-3_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27399-0

  • Online ISBN: 978-3-319-27400-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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