IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v13y2017i2p45-62.html
   My bibliography  Save this article

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

Author

Listed:
  • Francisco Javier Villar Martín

    (Department of Computer Science, University of Alcalá, Alcalá de Henares, Spain)

  • Jose Luis Castillo Sequera

    (Department of Computer Science, University of Alcalá, Alcalá de Henares, Spain)

  • Miguel Angel Navarro Huerga

    (Department of Computer Science, University of Alcalá, Alcalá de Henares, Spain)

Abstract

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

Suggested Citation

  • Francisco Javier Villar Martín & Jose Luis Castillo Sequera & Miguel Angel Navarro Huerga, 2017. "Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 13(2), pages 45-62, April.
    • Handle: RePEc:igg:jdwm00:v:13:y:2017:i:2:p:45-62
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.2017040103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jdwm00:v:13:y:2017:i:2:p:45-62. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.