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Exemplar-Based Clustering via Simulated Annealing

Published online by Cambridge University Press:  01 January 2025

Michael J. Brusco and
Hans-Friedrich Köhn
Michael J. Brusco*
Affiliation:
Florida State University
Hans-Friedrich Köhn
Affiliation:
University of Missouri-Columbia
*
Requests for reprints should be sent to Michael J. Brusco, Department of Marketing, College of Business, Florida State University, Tallahassee, FL 32306-1110, USA. E-mail: mbrusco@cob.fsu.edu
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Abstract

Several authors have touted the p-median model as a plausible alternative to within-cluster sums of squares (i.e., K-means) partitioning. Purported advantages of the p-median model include the provision of “exemplars” as cluster centers, robustness with respect to outliers, and the accommodation of a diverse range of similarity data. We developed a new simulated annealing heuristic for the p-median problem and completed a thorough investigation of its computational performance. The salient findings from our experiments are that our new method substantially outperforms a previous implementation of simulated annealing and is competitive with the most effective metaheuristics for the p-median problem.

Keywords

cluster analysispartitioningheuristicsp-median modelsimulated annealing

Information

Type
Theory and Methods
Information
Psychometrika , Volume 74 , Issue 3 , September 2009, pp. 457 - 475
Copyright
Copyright © 2009 The Psychometric Society

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Footnotes

An erratum to this article can be found at http://dx.doi.org/10.1007/s11336-009-9140-1

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