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Nature Inspired Data Mining Algorithm for Document Clustering in Information Retrieval

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Information Retrieval Technology (AIRS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8870))

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Abstract

Document clustering is an important technique that has been widely employed in Information Retrieval (IR). Various clustering techniques have been reported, but the effectiveness of most techniques relies on the initial value of k clusters. Such an approach may not be suitable as we may not have prior knowledge on the collection of documents. To date, there are various swarm based clustering techniques proposed to address such problem, including this paper that explores the adaptation of Firefly Algorithm (FA) in document clustering. We extend the work on Gravitation Firefly Algorithm (GFA) by introducing a relocate mechanism that relocates assigned documents, if necessary. The newly proposed clustering algorithm, known as GFA_R, is then tested on a benchmark dataset obtained from the 20Newsgroups. Experimental results on external and relative quality metrics for the GFA_R is compared against the one obtained using the standard GFA and Bisect K-means. It is learned that by extending GFA to becoming GFA_R, a better quality clustering is obtained.

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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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Editor information

Editors and Affiliations

  1. Institute of Visual Informatic, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Azizah Jaafar

  2. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Nazlena Mohamad Ali

  3. Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Shahrul Azman Mohd Noah

  4. Insight Centre for Data Analytics, Dublin City University, Glasnevin, 9, Dublin, Ireland

    Alan F. Smeaton

  5. Information Systems, Queensland University of Technology, 4001, Brisbane, QLD, Australia

    Peter Bruza

  6. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

    Zainab Abu Bakar  & Nursuriati Jamil  & 

  7. Cyber Security Center, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, 57000, Kuala Lumpur, Malaysia

    Tengku Mohd Tengku Sembok

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© 2014 Springer International Publishing Switzerland

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Mohammed, A.J., Yusof, Y., Husni, H. (2014). Nature Inspired Data Mining Algorithm for Document Clustering in Information Retrieval. In: Jaafar, A., et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_33

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  • DOI: https://doi.org/10.1007/978-3-319-12844-3_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12843-6

  • Online ISBN: 978-3-319-12844-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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