Can the extent of variability within a dataset be reflected through clustering?
I think it would make sense to compare the distribution (and means) of standard deviations for each variable between the groups. So the mean of the standard deviation for each era would be something like:
$
\overline{\sigma}_{Era} = \frac{\sum_{i = (features)}  \sigma_{i}}{n}
$
where $n$ is the number of features for each era, and $ \sigma_{i} $ is the standard deviation for one feature of the era.
I think interpreting the results of this would be more straight forward and would be simple to compute. It would also be easy to visualize, as you could just make a box plot for the feature standard deviations for each architectural era and you'd be able to easily see which era has the greatest variance.