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Constrained classification: The use of a priori information in cluster analysis

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  • Wayne DeSarbo
  • Vijay Mahajan

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  • Wayne DeSarbo & Vijay Mahajan, 1984. "Constrained classification: The use of a priori information in cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 49(2), pages 187-215, June.
    • Handle: RePEc:spr:psycho:v:49:y:1984:i:2:p:187-215
    DOI: 10.1007/BF02294172
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    References listed on IDEAS

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    1. John M. Mulvey & Harlan P. Crowder, 1979. "Cluster Analysis: An Application of Lagrangian Relaxation," Management Science, INFORMS, vol. 25(4), pages 329-340, April.
    2. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    3. Shmuel Sattath & Amos Tversky, 1977. "Additive similarity trees," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 319-345, September.
    4. Anuška Ferligoj & Vladimir Batagelj, 1983. "Some types of clustering with relational constraints," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 541-552, December.
    5. Ledyard Tucker, 1972. "Relations between multidimensional scaling and three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 3-27, March.
    6. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, 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.
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    Cited by:

    1. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Aspectos Metodológicos da Segmentação de Mercado: Base de Segmentação e Métodos de Classificação," FEP Working Papers 261, Universidade do Porto, Faculdade de Economia do Porto.
    2. Giuseppe RICCIARDO LAMONICA, 2002. "La funzionalita' nelle zone omogenee delle Marche," Working Papers 165, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Yu Ding & Wayne S. DeSarbo & Dominique M. Hanssens & Kamel Jedidi & John G. Lynch & Donald R. Lehmann, 2020. "The past, present, and future of measurement and methods in marketing analysis," Marketing Letters, Springer, vol. 31(2), pages 175-186, September.
    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. 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.
    6. repec:dgr:rugsom:96b34 is not listed on IDEAS
    7. Wedel, Michel & DeSarbo, Wayne S., 1996. "Semiparametric estimation of (constrained) ultrametric trees," Research Report 96B34, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    8. Igor Melnykov & Volodymyr Melnykov, 2020. "A Note on the Formal Implementation of the K-means Algorithm with Hard Positive and Negative Constraints," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 789-809, October.
    9. 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.
    10. Naoto Yamashita & Kohei Adachi, 2020. "A Modified k-Means Clustering Procedure for Obtaining a Cardinality-Constrained Centroid Matrix," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 509-525, July.
    11. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    12. 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.
    13. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    14. Wayne S. DeSarbo & Qian Chen & Ashley Stadler Blank, 2017. "A Parametric Constrained Segmentation Methodology for Application in Sport Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 4(4), pages 37-55, December.

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    Keywords

    Cluster Analysis; Constrained Optimization;

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