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The Heterogeneous P-Median Problem for Categorization Based Clustering

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  • Simon Blanchard

    ()

  • Daniel Aloise

    ()

  • Wayne DeSarbo

    ()

Abstract

The p-median offers an alternative to centroid-based clustering algorithms for identifying unobserved categories. However, existing p-median formulations typically require data aggregation into a single proximity matrix, resulting in masked respondent heterogeneity. A proposed three-way formulation of the p-median problem explicitly considers heterogeneity by identifying groups of individual respondents that perceive similar category structures. Three proposed heuristics for the heterogeneous p-median (HPM) are developed and then illustrated in a consumer psychology context using a sample of undergraduate students who performed a sorting task of major U.S. retailers, as well as a through Monte Carlo analysis. Copyright The Psychometric Society 2012

Suggested Citation

  • Simon Blanchard & Daniel Aloise & Wayne DeSarbo, 2012. "The Heterogeneous P-Median Problem for Categorization Based Clustering," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 741-762, October.
  • Handle: RePEc:spr:psycho:v:77:y:2012:i:4:p:741-762
    DOI: 10.1007/s11336-012-9283-3
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    References listed on IDEAS

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    1. repec:spr:jclass:v:34:y:2017:i:2:d:10.1007_s00357-017-9231-0 is not listed on IDEAS
    2. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    3. Santi, Éverton & Aloise, Daniel & Blanchard, Simon J., 2016. "A model for clustering data from heterogeneous dissimilarities," European Journal of Operational Research, Elsevier, vol. 253(3), pages 659-672.
    4. Michael Brusco & Douglas Steinley, 2015. "Affinity Propagation and Uncapacitated Facility Location Problems," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 443-480, October.

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