IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v6y1989i1p53-71.html
   My bibliography  Save this article

A validation study of a variable weighting algorithm for cluster analysis

Author

Listed:
  • Glenn Milligan

Abstract

No abstract is available for this item.

Suggested Citation

  • Glenn Milligan, 1989. "A validation study of a variable weighting algorithm for cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 6(1), pages 53-71, December.
    • Handle: RePEc:spr:jclass:v:6:y:1989:i:1:p:53-71
    DOI: 10.1007/BF01908588
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF01908588
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF01908588?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. Glenn Milligan, 1985. "An algorithm for generating artificial test clusters," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 123-127, March.
    2. Geert Soete, 1986. "Optimal variable weighting for ultrametric and additive tree clustering," Quality & Quantity: International Journal of Methodology, Springer, vol. 20(2), pages 169-180, June.
    3. Glenn Milligan, 1979. "Ultrametric hierarchical clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 44(3), pages 343-346, 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. 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.
    2. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    3. Paul Green & Jonathan Kim & Frank Carmone, 1990. "A preliminary study of optimal variable weighting in k-means clustering," Journal of Classification, Springer;The Classification Society, vol. 7(2), pages 271-285, September.
    4. Anzanello, Michel J. & Fogliatto, Flavio S., 2011. "Selecting the best clustering variables for grouping mass-customized products involving workers' learning," International Journal of Production Economics, Elsevier, vol. 130(2), pages 268-276, April.
    5. Susan Brudvig & Michael J. Brusco & J. Dennis Cradit, 2019. "Joint selection of variables and clusters: recovering the underlying structure of marketing data," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(1), pages 1-12, March.
    6. 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.
    7. 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.

    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. 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.
    2. Paul Green & Jonathan Kim & Frank Carmone, 1990. "A preliminary study of optimal variable weighting in k-means clustering," Journal of Classification, Springer;The Classification Society, vol. 7(2), pages 271-285, September.
    3. Renato Amorim, 2015. "Feature Relevance in Ward’s Hierarchical Clustering Using the L p Norm," Journal of Classification, Springer;The Classification Society, vol. 32(1), pages 46-62, April.
    4. Glenn Milligan & Richard Cheng, 1996. "Measuring the influence of individual data points in a cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 315-335, September.
    5. Mingoti, Sueli A. & Lima, Joab O., 2006. "Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1742-1759, November.
    6. 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.
    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. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    9. Douglas L. Steinley, 2019. "Editorial: Journal of Classification Vol. 36-3," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 393-396, October.
    10. Niels Waller & Heather Kaiser & Janine Illian & Mike Manry, 1998. "A comparison of the classification capabilities of the 1-dimensional kohonen neural network with two pratitioning and three hierarchical cluster analysis algorithms," Psychometrika, Springer;The Psychometric Society, vol. 63(1), pages 5-22, March.
    11. Chen, S. K. & Mangiameli, P. & West, D., 1995. "The comparative ability of self-organizing neural networks to define cluster structure," Omega, Elsevier, vol. 23(3), pages 271-279, June.
    12. Glenn Milligan, 1980. "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 325-342, September.
    13. Gheorghe H. Popescu & Violeta Sima & Elvira Nica & Ileana Georgiana Gheorghe, 2017. "Measuring Sustainable Competitiveness in Contemporary Economies—Insights from European Economy," Sustainability, MDPI, vol. 9(7), pages 1-26, July.
    14. R. Gnanadesikan & J. Kettenring & S. Tsao, 1995. "Weighting and selection of variables for cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 113-136, March.
    15. Roberto Rocci & Maurizio Vichi, 2005. "Three-Mode Component Analysis with Crisp or Fuzzy Partition of Units," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 715-736, December.
    16. Balakrishnan, P. V. (Sundar) & Cooper, Martha C. & Jacob, Varghese S. & Lewis, Phillip A., 1996. "Comparative performance of the FSCL neural net and K-means algorithm for market segmentation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 346-357, September.
    17. Jerzy Korzeniewski, 2013. "Empirical Evaluation of OCLUS and GenRandomClust Algorithms of Generating Cluster Structures," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 487-494, September.
    18. Joeri Hofmans & Eva Ceulemans & Douglas Steinley & Iven Mechelen, 2015. "On the Added Value of Bootstrap Analysis for K-Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 268-284, July.
    19. 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.
    20. J. Fernando Vera & Rodrigo Macías, 2017. "Variance-Based Cluster Selection Criteria in a K-Means Framework for One-Mode Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 275-294, June.

    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:6:y:1989:i:1:p:53-71. 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.