IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v54y1989i4p699-706.html
   My bibliography  Save this article

Clusteringn objects intok groups under optimal scaling of variables

Author

Listed:
  • Stef Buuren
  • Willem Heiser

Abstract

No abstract is available for this item.

Suggested Citation

  • Stef Buuren & Willem Heiser, 1989. "Clusteringn objects intok groups under optimal scaling of variables," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 699-706, September.
    • Handle: RePEc:spr:psycho:v:54:y:1989:i:4:p:699-706
    DOI: 10.1007/BF02296404
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02296404
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02296404?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. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    2. Forrest Young, 1981. "Quantitative analysis of qualitative data," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 357-388, December.
    3. Shizuhiko Nishisato, 1984. "Forced classification: A simple application of a quantification method," Psychometrika, Springer;The Psychometric Society, vol. 49(1), pages 25-36, March.
    4. Shizuhiko Zishisato, 1984. "Forced classification: A simple application of a quantification method," Psychometrika, Springer;The Psychometric Society, vol. 49(3), pages 437-437, September.
    5. Hans Bock, 1972. "Statistische Modelle und Bayessche Verfahren zur Bestimmung einer unbekannten Klassifikation normalverteilter zufälliger Vektoren," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 18(1), pages 120-132, December.
    6. Wayne DeSarbo & J. Carroll & Linda Clark & Paul Green, 1984. "Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables," Psychometrika, Springer;The Psychometric Society, vol. 49(1), pages 57-78, March.
    7. J. M. Liittschwager & C. Wang, 1978. "Integer Programming Solution of a Classification Problem," Management Science, INFORMS, vol. 24(14), pages 1515-1525, October.
    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. Masaki Mitsuhiro & Hiroshi Yadohisa, 2015. "Reduced $$k$$ k -means clustering with MCA in a low-dimensional space," Computational Statistics, Springer, vol. 30(2), pages 463-475, June.
    2. DeSarbo, Wayne S. & Selin Atalay, A. & Blanchard, Simon J., 2009. "A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3217-3230, June.
    3. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    4. Monia Ranalli & Roberto Rocci, 2017. "A Model-Based Approach to Simultaneous Clustering and Dimensional Reduction of Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1007-1034, December.
    5. Groothuis-Oudshoorn, Catharina G.M. & Chorus, Astrid M.J. & Taeke van Beekum, W. & Detmar, Symone B. & van den Hout, Wilbert B., 2006. "Modelling and estimation of valuations for the Dutch London Handicap Scale," Journal of Health Economics, Elsevier, vol. 25(6), pages 1119-1138, November.

    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. Michael Windham & J. Hutchinson & Shizuhiko Nishisato & Ludovic Lebart & George Furnas & Richard Dubes & Frank Critchley & A. Gordon & Fionn Murtagh & Ulf Bockenholt & Philip Hopke & Daniel Wartenberg, 1988. "Book reviews," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 105-154, March.
    2. Wayne DeSarbo & Richard Oliver & Arvind Rangaswamy, 1989. "A simulated annealing methodology for clusterwise linear regression," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 707-736, September.
    3. T. Jefferson & J. May & N. Ravi, 1989. "An entropy approach to the scaling of ordinal categorical data," Psychometrika, Springer;The Psychometric Society, vol. 54(2), pages 203-215, June.
    4. Dolnicar, Sara & Grün, Bettina & Leisch, Friedrich, 2016. "Increasing sample size compensates for data problems in segmentation studies," Journal of Business Research, Elsevier, vol. 69(2), pages 992-999.
    5. V. Choulakian, 2008. "Taxicab Correspondence Analysis of Contingency Tables with One Heavyweight Column," Psychometrika, Springer;The Psychometric Society, vol. 73(2), pages 309-319, June.
    6. Michael Brusco & J. Cradit, 2001. "A variable-selection heuristic for K-means clustering," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 249-270, June.
    7. Douglas Steinley & Michael Brusco, 2008. "Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures," Psychometrika, Springer;The Psychometric Society, vol. 73(1), pages 125-144, March.
    8. Maarten M. Kampert & Jacqueline J. Meulman & Jerome H. Friedman, 2017. "rCOSA: A Software Package for Clustering Objects on Subsets of Attributes," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 514-547, October.
    9. Ehtibar Dzhafarov & Hans Colonius, 2006. "Reconstructing Distances among Objects from Their Discriminability," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 365-386, June.
    10. Shizuhiko Nishisato, 1996. "Gleaning in the field of dual scaling," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 559-599, December.
    11. Willem Heiser, 2013. "In memoriam, J. Douglas Carroll 1939–2011," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 5-13, January.
    12. Renato Cordeiro Amorim, 2016. "A Survey on Feature Weighting Based K-Means Algorithms," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 210-242, July.
    13. Balepur, Prashant Narayan, 1998. "Impacts of Computer-Mediated Communication on Travel and Communication Patterns: The Davis Community Network Study," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6cb1f85c, Institute of Transportation Studies, UC Berkeley.
    14. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    15. Naomichi Makino, 2015. "Generalized data-fitting factor analysis with multiple quantification of categorical variables," Computational Statistics, Springer, vol. 30(1), pages 279-292, March.
    16. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.
    17. J. Fernando Vera & Rodrigo Macías, 2021. "On the Behaviour of K-Means Clustering of a Dissimilarity Matrix by Means of Full Multidimensional Scaling," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 489-513, June.
    18. van Rosmalen, J.M. & Koning, A.J. & Groenen, P.J.F., 2007. "Optimal Scaling of Interaction Effects in Generalized Linear Models," Econometric Institute Research Papers EI 2007-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Geert Soete & J. Carroll & Wayne DeSarbo, 1987. "Least squares algorithms for constructing constrained ultrametric and additive tree representations of symmetric proximity data," Journal of Classification, Springer;The Classification Society, vol. 4(2), pages 155-173, September.
    20. Florian Schuberth & Jörg Henseler & Theo K. Dijkstra, 2018. "Partial least squares path modeling using ordinal categorical indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 9-35, January.

    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:54:y:1989:i:4:p:699-706. 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.