IDEAS home Printed from
   My bibliography  Save this article

Implementation of Chaid Algorithm: A Hotel Case


  • Celal Hakan Kagnicioglu

    (AnadoluUniversity, Faculty of Economics and Administrative Sciences, Department of Business Administration, Eskisehir, 26170, Turkey)

  • Mune Mogol

    (Anadolu University, Tourism Faculty, Department of Tourism Management, Eskisehir, 26170, Turkey)


Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM) tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID) algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation. Key Words:Data Mining, CHAID, Tourism, Hotel

Suggested Citation

  • Celal Hakan Kagnicioglu & Mune Mogol, 2014. "Implementation of Chaid Algorithm: A Hotel Case," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 3(4), pages 42-51, October.
  • Handle: RePEc:rbs:ijbrss:v:3:y:2014:i:4:p:42-51

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Antipov, Evgeny & Pokryshevskaya, Elena, 2009. "Applying CHAID for logistic regression diagnostics and classification accuracy improvement," MPRA Paper 21499, University Library of Munich, Germany.
    2. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
    3. repec:bla:jorssc:v:29:y:1980:i:2:p:119-127 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    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:rbs:ijbrss:v:3:y:2014:i:4:p:42-51. 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: (Umit Hacioglu). 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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.