The Heterogeneous P-Median Problem for Categorization Based Clustering

Simon J. Blanchard, Daniel Aloise, Wayne Desarbo

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)741-762
Number of pages22
Issue number4
StatePublished - Oct 1 2012

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics


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