Remaining within-cluster heterogeneity: A meta-analysis of the "dark side" of clustering methods

Nikolaus Franke, Heribert Reisinger, Daniel Hoppe

Publication: Scientific journalJournal articlepeer-review

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Abstract

In a meta-analysis of articles employing clustering methods, we find that little attention is paid to remaining within-cluster heterogeneity and that average values are relatively high. We suggest addressing this potentially problematic "dark side" of cluster analysis by providing two coefficients as standard information in any cluster analysis findings: a goodness-of-fit measure and a measure which relates explained variation of analysed empirical data to explained variation of simulated random data. The second coefficient is referred to as the Index of Clustering Appropriateness (ICA). Finally, we develop a classification scheme depicting acceptable levels of remaining within-cluster heterogeneity.
Original languageEnglish
Pages (from-to)273 - 293
JournalJournal of Marketing Management
Volume25
Issue number3-4
DOIs
Publication statusPublished - 2009

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