Abstract
A common descriptive statistic in cluster analysis is the R2 that measures the overall proportion of variance explained by the cluster means. This note highlights properties of the R2 for clustering. In particular, we show that generally the R2 can be artificially inflated by linearly transforming the data by “stretching” and by projecting. Also, the R2 for clustering will often be a poor measure of clustering quality in high‐dimensional settings. We also investigate the R2 for clustering for misspecified models. Several simulation illustrations are provided highlighting weaknesses in the clustering R2, especially in high‐dimensional settings. A functional data example is given showing how that R2 for clustering can vary dramatically depending on how the curves are estimated.
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