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

Nikolaus Franke, Heribert Reisinger, Daniel Hoppe

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

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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.
OriginalspracheEnglisch
Seiten (von - bis)273 - 293
FachzeitschriftJournal of Marketing Management
Jahrgang25
Ausgabenummer3-4
DOIs
PublikationsstatusVeröffentlicht - 2009

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