  We propose a new approach for scaling prior to cluster analysis based on the
concept of pooled variance. Unlike available scaling procedures such as the
standard deviation and the range, our proposed scale avoids dampening the
beneficial effect of informative clustering variables. We confirm through an
extensive simulation study and applications to well known real data examples
that the proposed scaling method is safe and generally useful. Finally, we use
our approach to cluster a high dimensional genomic dataset consisting of gene
expression data for several specimens of breast cancer cells tissue.
