IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v47y1982i4p413-426.html
   My bibliography  Save this article

Clustering with relational constraint

Author

Listed:
  • Anuška Ferligoj
  • Vladimir Batagelj

Abstract

No abstract is available for this item.

Suggested Citation

  • Anuška Ferligoj & Vladimir Batagelj, 1982. "Clustering with relational constraint," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 413-426, December.
  • Handle: RePEc:spr:psycho:v:47:y:1982:i:4:p:413-426
    DOI: 10.1007/BF02293706
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02293706
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/BF02293706?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.

    References listed on IDEAS

    as
    1. Vladimir Batagelj, 1981. "Note on ultrametric hierarchical clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 46(3), pages 351-352, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Juan Carlos Duque & Raul Ramos Lobo & Jordi Surinach Caralt, 2004. "Design of Homogeneous Territorial Units: A Methodological Proposal," Working Papers in Economics 115, Universitat de Barcelona. Espai de Recerca en Economia.
    2. Maravalle, Maurizio & Simeone, Bruno & Naldini, Rosella, 1997. "Clustering on trees," Computational Statistics & Data Analysis, Elsevier, vol. 24(2), pages 217-234, April.
    3. Renato Coppi & Pierpaolo D’Urso & Paolo Giordani, 2010. "A Fuzzy Clustering Model for Multivariate Spatial Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 54-88, March.
    4. Juan Carlos Duque & Raúl Ramos & Jordi Suriñach, 2007. "Supervised Regionalization Methods: A Survey," International Regional Science Review, , vol. 30(3), pages 195-220, July.
    5. Andrzej Młodak, 2021. "k-Means, Ward and Probabilistic Distance-Based Clustering Methods with Contiguity Constraint," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 313-352, July.
    6. Rui Fragoso & Conceição Rego & Vladimir Bushenkov, 2016. "Clustering of Territorial Areas: A Multi-Criteria Districting Problem," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(2), pages 179-198, December.
    7. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
    8. G. Damiana Costanzo, 2001. "A constrainedk-means clustering algorithm for classifying spatial units," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 237-256, January.
    9. Dongyoung Kim & Sungwon Jung & Yongwook Jeong, 2021. "Theft Prediction Model Based on Spatial Clustering to Reflect Spatial Characteristics of Adjacent Lands," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    10. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    11. Nathanaël Randriamihamison & Nathalie Vialaneix & Pierre Neuvial, 2021. "Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 363-389, July.
    12. Giuseppe Giordano & Maria Vitale, 2011. "On the use of external information in social network analysis," 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. 5(2), pages 95-112, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. William Day & Herbert Edelsbrunner, 1985. "Investigation of proportional link linkage clustering methods," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 239-254, December.
    2. Górecki, Jan & Hofert, Marius & Okhrin, Ostap, 2021. "Outer power transformations of hierarchical Archimedean copulas: Construction, sampling and estimation," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    3. Werner Vach & Paul Degens, 1991. "A new approach to isotonic agglomerative hierarchical clustering," Journal of Classification, Springer;The Classification Society, vol. 8(2), pages 217-237, December.
    4. Nathanaël Randriamihamison & Nathalie Vialaneix & Pierre Neuvial, 2021. "Applicability and Interpretability of Ward’s Hierarchical Agglomerative Clustering With or Without Contiguity Constraints," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 363-389, July.
    5. Akinori Okada & Hans-Hermann Bock & F. Murtagh & F. Rohlf & Wei-Chien Chang & Shizuhiko Nishisato & Robert Sokal & Carolyn Anderson & Frank Critchley & Frank Critchley & Robert Golden, 1989. "Book reviews," Journal of Classification, Springer;The Classification Society, vol. 6(1), pages 121-161, December.
    6. Takeuchi, Akinobu & Yadohisa, Hiroshi & Inada, Koichi, 2001. "Space distortion and monotone admissibility in agglomerative clustering," SFB 373 Discussion Papers 2001,78, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. William Day & Herbert Edelsbrunner, 1984. "Efficient algorithms for agglomerative hierarchical clustering methods," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 7-24, December.

    More about this item

    Keywords

    optimization approach to clustering;

    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:psycho:v:47:y:1982:i:4:p:413-426. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.