Exemplar-based clustering via simulated annealing

Michael J. Brusco, Hans Friedrich Köhn

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)457-475
Number of pages19
Issue number3
StatePublished - Sep 2009
Externally publishedYes


  • Cluster analysis
  • Heuristics
  • P-median model
  • Partitioning
  • Simulated annealing

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics


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