IDEAS home Printed from https://ideas.repec.org/a/igg/jkm000/v16y2020i3p70-88.html
   My bibliography  Save this article

Discovering Knowledge by Comparing Silhouettes Using K-Means Clustering for Customer Segmentation

Author

Listed:
  • Zeeshan Akbar

    (Xian Jiaotong University, China)

  • Jun Liu

    (Xian Jiaotong University, China)

  • Zahida Latif

    (CASE, Isalamabad, Pakistan)

Abstract

A large amount of data is generated every day from different sources. Knowledge extraction is the discovery of some useful and potential information from data that can help to make better decisions. Today's business process requires a technique that is intelligent and has the capability to discover useful patterns in data called data mining. This research is about using silhouettes created from K-means clustering to extract knowledge. This paper implements K-means clustering technique in order to group customers into K clusters according to deals purchased in two different scenarios using evolutionary algorithm for optimization and compare silhouettes for different K values to analyze the improvement in extracted knowledge.

Suggested Citation

  • Zeeshan Akbar & Jun Liu & Zahida Latif, 2020. "Discovering Knowledge by Comparing Silhouettes Using K-Means Clustering for Customer Segmentation," International Journal of Knowledge Management (IJKM), IGI Global, vol. 16(3), pages 70-88, July.
    • Handle: RePEc:igg:jkm000:v:16:y:2020:i:3:p:70-88
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKM.2020070105
    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:jkm000:v:16:y:2020:i:3:p:70-88. 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.