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Experimental Analysis of Firefly Algorithms for Divisive Clustering of Web Documents

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Recent Advances on Soft Computing and Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 287))

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Abstract

This paper studies two clustering algorithms that are based on the Firefly Algorithm (FA) which is a recent swarm intelligence approach. We perform experiments utilizing the Newton’s Universal Gravitation Inspired Firefly Algorithm (GFA) and Weight-Based Firefly Algorithm (WFA) on the 20_newsgroups dataset. The analysis is undertaken on two parameters. The first is the alpha (α) value in the Firefly algorithms and latter is the threshold value required during clustering process. Results showed that a better performance is demonstrated by Weight-Based Firefly Algorithm compared to Newton’s Universal Gravitation Inspired Firefly Algorithm.

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Author information

Authors and Affiliations

  1. School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010, Sintok, Kedah, Malaysia

    Athraa Jasim Mohammed, Yuhanis Yusof & Husniza Husni

  2. Information and Communication Technology Center, University of Technology, Baghdad, Iraq

    Athraa Jasim Mohammed

Authors
  1. Athraa Jasim Mohammed
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  2. Yuhanis Yusof
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  3. Husniza Husni
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Corresponding author

Correspondence to Athraa Jasim Mohammed .

Editor information

Editors and Affiliations

  1. Department of Information System Faculty of Comp. Sci. & Info. Tech., University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Faculty of Comp. Sci. and Info. Tech, Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Rozaida Ghazali

  3. Faculty of Comp. Sci. and Info. Tech., Universiti Tun Hussein Onn Malaysia, Parit Raja, Malaysia

    Mustafa Mat Deris

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Mohammed, A.J., Yusof, Y., Husni, H. (2014). Experimental Analysis of Firefly Algorithms for Divisive Clustering of Web Documents. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_46

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  • DOI: https://doi.org/10.1007/978-3-319-07692-8_46

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07691-1

  • Online ISBN: 978-3-319-07692-8

  • eBook Packages: EngineeringEngineering (R0)

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