IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-319-99142-9_13.html
   My bibliography  Save this book chapter

Review of Basic Local Searches for Solving the Minimum Sum-of-Squares Clustering Problem

In: Open Problems in Optimization and Data Analysis

Author

Listed:
  • Thiago Pereira

    (Universidade Federal do Rio Grande do Norte)

  • Daniel Aloise

    (Polytechnique Montréal)

  • Jack Brimberg

    (The Royal Military College of Canada)

  • Nenad Mladenović

    (Emirates College of Technologies
    Mathematical Institute)

Abstract

This paper presents a review of the well-known K-means, H-means, and J-means heuristics, and their variants, that are used to solve the minimum sum-of-squares clustering problem. We then develop two new local searches that combine these heuristics in a nested and sequential structure, also referred to as variable neighborhood descent. In order to show how these local searches can be implemented within a metaheuristic framework, we apply the new heuristics in the local improvement step of two variable neighborhood search (VNS) procedures. Computational experiments are carried out which suggest that this new and simple application of VNS is comparable to the state of the art. In addition, a very significant improvement (over 30%) in solution quality is obtained for the largest problem instance investigated containing 85,900 entities.

Suggested Citation

  • Thiago Pereira & Daniel Aloise & Jack Brimberg & Nenad Mladenović, 2018. "Review of Basic Local Searches for Solving the Minimum Sum-of-Squares Clustering Problem," Springer Optimization and Its Applications, in: Panos M. Pardalos & Athanasios Migdalas (ed.), Open Problems in Optimization and Data Analysis, pages 249-270, Springer.
  • Handle: RePEc:spr:spochp:978-3-319-99142-9_13
    DOI: 10.1007/978-3-319-99142-9_13
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pawel Kalczynski & Zvi Goldstein & Zvi Drezner, 2023. "An Efficient Heuristic for the k-Partitioning Problem," SN Operations Research Forum, Springer, vol. 4(4), pages 1-21, December.

    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:spr:spochp:978-3-319-99142-9_13. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.