IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v13y1996i1p157-168.html
   My bibliography  Save this article

Space-conserving agglomerative algorithms

Author

Listed:
  • Zhenmin Chen
  • John Ness

Abstract

No abstract is available for this item.

Suggested Citation

  • Zhenmin Chen & John Ness, 1996. "Space-conserving agglomerative algorithms," Journal of Classification, Springer;The Classification Society, vol. 13(1), pages 157-168, March.
    • Handle: RePEc:spr:jclass:v:13:y:1996:i:1:p:157-168
    DOI: 10.1007/BF01202586
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF01202586
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF01202586?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. Andreea B. Dragut, 2012. "Stock Data Clustering and Multiscale Trend Detection," Methodology and Computing in Applied Probability, Springer, vol. 14(1), pages 87-105, March.
    2. Pavel I. Blus & Rustam V. Plotnikov, 2022. "Spatial clustering for reducing intraregional unevenness," Journal of New Economy, Ural State University of Economics, vol. 23(1), pages 88-108, April.
    3. Alan Lee & Bobby Willcox, 2014. "Minkowski Generalizations of Ward’s Method in Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 194-218, July.
    4. Gautier Marti & S'ebastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Papers 1603.04017, arXiv.org, revised Apr 2016.
    5. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    6. Gautier Marti & Sébastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Post-Print hal-01400395, HAL.
    7. Trudie Strauss & Michael Johan von Maltitz, 2017. "Generalising Ward’s Method for Use with Manhattan Distances," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
    8. J.-P. Barthélemy & F. Brucker & C. Osswald, 2007. "Combinatorial optimisation and hierarchical classifications," Annals of Operations Research, Springer, vol. 153(1), pages 179-214, September.

    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:13:y:1996:i:1:p:157-168. 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.