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Marketing Letters

, Volume 21, Issue 1, pp 83–101 | Cite as

Evaluation of structure and reproducibility of cluster solutions using the bootstrap

  • Sara Dolnicar
  • Friedrich Leisch
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.

Keywords

Cluster analysis Mixture models Bootstrap 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Marketing Research Innovation Centre (MRIC), School of Management and MarketingUniversity of WollongongWollongongAustralia
  2. 2.Department of StatisticsLudwig-Maximilians-Universität MünchenMunichGermany

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