The Heterogeneous P-Median Problem for Categorization Based Clustering

48 Pages Posted: 31 Mar 2012 Last revised: 17 Jun 2016

See all articles by Simon J. Blanchard

Simon J. Blanchard

Georgetown University - Robert Emmett McDonough School of Business

Daniel Aloise

Universidade Federal do Rio Grande do Norte (UFRN)

Wayne S. DeSarbo

Pennsylvania State University

Date Written: March 29, 2012

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 through a Monte Carlo analysis.

Keywords: p-median, heterogeneity, sorting task, categorization, clustering, consumer psychology

JEL Classification: C6, C60, M31, M30

Suggested Citation

Blanchard, Simon J. and Aloise, Daniel and DeSarbo, Wayne S., The Heterogeneous P-Median Problem for Categorization Based Clustering (March 29, 2012). Psychometrika, Forthcoming, Georgetown McDonough School of Business Research Paper No 2012-11, Available at SSRN: https://ssrn.com/abstract=2030830

Simon J. Blanchard (Contact Author)

Georgetown University - Robert Emmett McDonough School of Business ( email )

3700 O Street, NW
Washington, DC 20057
United States

Daniel Aloise

Universidade Federal do Rio Grande do Norte (UFRN) ( email )

PO Box 1524
Natal-RN, 59078970
Brazil

Wayne S. DeSarbo

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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