Evaluation of structure and reproducibility of cluster solutions using the bootstrap

Marketing Letters volume 21, pages83–101(2010)Cite this article

Abstract

Segmentation results derived using cluster analysis depend on (1) the structure of the data and (2) algorithm parameters. Typically, neither the data structure nor the sensitivity of the analysis to changes in algorithm parameters is assessed in advance of clustering. We propose a benchmarking framework based on bootstrapping techniques that accounts for sample and algorithm randomness. This provides much needed guidance both to data analysts and users of clustering solutions regarding the choice of the final clusters from computations that are exploratory in nature.

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Affiliations

  1. Marketing Research Innovation Centre (MRIC), School of Management and Marketing, University of Wollongong, Northfields Ave, Wollongong, New South Wales, 2522, Australia

    Sara Dolnicar & Friedrich Leisch

  2. Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstrasse 33, 80539, Munich, Germany

    Friedrich Leisch

Authors
  1. Sara Dolnicar
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  2. Friedrich Leisch
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Correspondence to Friedrich Leisch.

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Dolnicar, S., Leisch, F. Evaluation of structure and reproducibility of cluster solutions using the bootstrap. Mark Lett 21, 83–101 (2010). https://doi.org/10.1007/s11002-009-9083-4

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Keywords

  • Cluster analysis
  • Mixture models
  • Bootstrap