Evaluation of structure and reproducibility of cluster solutions using the bootstrap

Dolnicar, Sara and Leisch, Friedrich (2010) Evaluation of structure and reproducibility of cluster solutions using the bootstrap. Marketing Letters, 21 1: 83-101. doi:10.1007/s11002-009-9083-4

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Author Dolnicar, Sara
Leisch, Friedrich
Title Evaluation of structure and reproducibility of cluster solutions using the bootstrap
Journal name Marketing Letters   Check publisher's open access policy
ISSN 0923-0645
1573-059X
Publication date 2010-03-01
Sub-type Article (original research)
DOI 10.1007/s11002-009-9083-4
Volume 21
Issue 1
Start page 83
End page 101
Total pages 19
Place of publication New York, USA
Publisher Springer New York
Language eng
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.
Keyword Cluster analysis
Mixture models
Bootstrap
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Business School Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 32 times in Thomson Reuters Web of Science Article | Citations
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