IDEAS home Printed from
   My bibliography  Save this article

A statistical data mining approach in bacteriology for bacterial identification


  • S.M. Monzurur Rahman
  • F.A. Siddiky
  • Uma Shrestha


Statistical data mining is one of the popular research fields of exploring valuable information from the large number of collected data. Applying statistical data mining techniques in several fields like medicine, bioinformatics, business, and bacteriology can be beneficial. Among them, bacteriology is one of the promising fields where statistical data mining is hardly ever used. The main purpose of this paper is to demonstrate the contribution of statistical data mining in the field of bacteriology. The research problem named as bacterial identification from bacteriology that we handle in this paper is a special kind of classification problem of statistical data mining where the only single representation of every class is present in the dataset. After studying this research problem, this paper proposes a novel statistical data mining approach using the decision tree technique in bacterial identification with better performance. The experimental results show significantly less number of biochemical tests are needed in bacterial identification using this proposed approach than the conventional approach that is being followed currently in the biochemical laboratory. Thus, the proposed approach not only benefits microbiologists, but it also improves the traditional approach of bacterial identification by saving time, total cost, and manual labour involvements.

Suggested Citation

  • S.M. Monzurur Rahman & F.A. Siddiky & Uma Shrestha, 2011. "A statistical data mining approach in bacteriology for bacterial identification," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(2), pages 117-142.
  • Handle: RePEc:ids:injdan:v:3:y:2011:i:2:p:117-142

    Download full text from publisher

    File URL:
    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.


    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:injdan:v:3:y:2011:i:2:p:117-142. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Darren Simpson). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.