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The Heterogeneous P-Median Problem for Categorization Based Clustering

Published online by Cambridge University Press:  01 January 2025

Simon J. Blanchard ,
Daniel Aloise  and
Wayne S. DeSarbo
Simon J. Blanchard*
Affiliation:
McDonough School of Business, Georgetown University
Daniel Aloise
Affiliation:
Department of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte
Wayne S. DeSarbo
Affiliation:
Department of Marketing, Smeal College of Business, Pennsylvania State University
*
Requests for reprints should be sent to Simon J. Blanchard, McDonough School of Business, Georgetown University, 37th and O St. N.W., Washington, DC 20057, USA. E-mail: [email protected]
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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.

Keywords

p-medianheterogeneitysorting taskcategorizationclusteringconsumer psychology
Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 4 , October 2012 , pp. 741 - 762
Copyright
Copyright © 2012 The Psychometric Society

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