IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v8y2016i4p330-347.html
   My bibliography  Save this article

Discovering optimal clusters using firefly algorithm

Author

Listed:
  • Athraa Jasim Mohammed
  • Yuhanis Yusof
  • Husniza Husni

Abstract

Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge. A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster. In this paper, we present the WFA_selection algorithm that originates from weight-based firefly algorithm. The newly proposed WFA_selection merges selected clusters in order to produce a better quality of clusters. Experiments utilising the WFA and WFA_selection algorithms were conducted on the 20Newsgroups and Reuters-21578 benchmark dataset and the output were compared against bisect K-means and general stochastic clustering method (GSCM). Results demonstrate that the WFA_selection generates a more robust and compact clusters as compared to the WFA, bisect K-means and GSCM.

Suggested Citation

  • Athraa Jasim Mohammed & Yuhanis Yusof & Husniza Husni, 2016. "Discovering optimal clusters using firefly algorithm," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 8(4), pages 330-347.
    • Handle: RePEc:ids:ijdmmm:v:8:y:2016:i:4:p:330-347
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=81239
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saida Ishak Boushaki & Nadjet Kamel & Omar Bendjeghaba, 2018. "High-Dimensional Text Datasets Clustering Algorithm Based on Cuckoo Search and Latent Semantic Indexing," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 1-24, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijdmmm:v:8:y:2016:i:4:p:330-347. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.