IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v28y2023i2-3-4p139-153.html
   My bibliography  Save this article

The application of clustering algorithms in a new model of knitted garment talent training in the context of sustainable development

Author

Listed:
  • Jing Wang

Abstract

Under the concept of sustainable development, the innovation and development of the knitted garment industry is crucial. In order to enhance the core competitiveness of the knitted garment industry, the study proposes a talent training strategy for the knitted garment industry based on a clustering algorithm, and constructs a talent-training model. The clustering algorithm showed a significant clustering effect, with a clustering accuracy of 93.66% in the real dataset. The knitwear talent development model obtained through the clustering analysis was applied in practice, and the application of talent development was able to significantly increase the proportion of elite talent in the company. The above results show that in the knitted garment industry under the concept of sustainable development, cluster analysis can effectively build a talent-training program, which is of great value to the sustainable development of the knitted garment industry and the production industry.

Suggested Citation

  • Jing Wang, 2023. "The application of clustering algorithms in a new model of knitted garment talent training in the context of sustainable development," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 28(2/3/4), pages 139-153.
    • Handle: RePEc:ids:ijnvor:v:28:y:2023:i:2/3/4:p:139-153
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=133835
    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.

    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:ids:ijnvor:v:28:y:2023:i:2/3/4:p:139-153. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=22 .

    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.