IDEAS home Printed from https://ideas.repec.org/a/ejn/ejbmjr/v6y2018i2p33-47.html
   My bibliography  Save this article

Classification Of Household Users Using Information Technologies Based On C5.0 Algorithm

Author

Listed:
  • Guner Gozde Teksin

    (Istanbul Commerce University, Turkey)

  • Munevver Turanli

    (Istanbul Commerce University, Turkey)

Abstract

The use of machine learning and decision tree methods with respect to modelling of big data has been increasing gradually. The data mining has focused on some methods for obtaining the useful information from big data which has not been recognized. The data mining and statistics concentrates upon the identification of structure. The concept of machine learning which evolved out of data mining as a need consists of models and algorithms which analyzes big data and may make significant deductions from data by benefitting from statistics and software. It provides convenience to modelling the big data that the decision tree methods are nonparametric methods. In this paper, the classification tree was created based on C5.0 algorithm by using R programming language.

Suggested Citation

  • Guner Gozde Teksin & Munevver Turanli, 2018. "Classification Of Household Users Using Information Technologies Based On C5.0 Algorithm," Eurasian Journal of Business and Management, Eurasian Publications, vol. 6(2), pages 33-47.
  • Handle: RePEc:ejn:ejbmjr:v:6:y:2018:i:2:p:33-47
    as

    Download full text from publisher

    File URL: https://eurasianpublications.com/wp-content/uploads/2021/02/EJBM-6.2.4.pdf
    Download Restriction: no
    ---><---

    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:ejn:ejbmjr:v:6:y:2018:i:2:p:33-47. 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: Esra Barakli (email available below). General contact details of provider: .

    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.