IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v21y2004i2p231-253.html
   My bibliography  Save this article

Model-Based Clustering for Image Segmentation and Large Datasets via Sampling

Author

Listed:
  • Ron Wehrens
  • Lutgarde M.C. Buydens
  • Chris Fraley
  • Adrian E. Raftery

Abstract

No abstract is available for this item.

Suggested Citation

  • Ron Wehrens & Lutgarde M.C. Buydens & Chris Fraley & Adrian E. Raftery, 2004. "Model-Based Clustering for Image Segmentation and Large Datasets via Sampling," Journal of Classification, Springer;The Classification Society, vol. 21(2), pages 231-253, September.
    • Handle: RePEc:spr:jclass:v:21:y:2004:i:2:p:231-253
    DOI: 10.1007/s00357-004-0018-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00357-004-0018-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00357-004-0018-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. repec:jss:jstsof:18:i06 is not listed on IDEAS
    2. Fraley, Chris & Raftery, Adrian, 2007. "Model-based Methods of Classification: Using the mclust Software in Chemometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i06).
    3. Siow Hoo Leong & Seng Huat Ong, 2017. "Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-30, July.
    4. Warren C Jochem & Douglas R Leasure & Oliver Pannell & Heather R Chamberlain & Patricia Jones & Andrew J Tatem, 2021. "Classifying settlement types from multi-scale spatial patterns of building footprints," Environment and Planning B, , vol. 48(5), pages 1161-1179, June.
    5. Yuhong Wei & Paul McNicholas, 2015. "Mixture model averaging for clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(2), pages 197-217, June.

    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:spr:jclass:v:21:y:2004:i:2:p:231-253. 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.