  Missing values are largely inevitable in gene expression microarray studies.
Data sets often have significant omissions due to individuals dropping out of
experiments, errors in data collection, image corruptions, and so on. Missing
data could potentially undermine the validity of research results - leading to
inaccurate predictive models and misleading conclusions. Imputation - a
relatively flexible, general purpose approach towards dealing with missing data
- is now available in massive numbers, making it possible to handle missing
data. While these estimation methods are becoming increasingly more effective
in resolving the discrepancies between true and estimated values, its effect on
clustering outcomes is largely disregarded.
  This study seeks to reveal the vast differences in agglomerative hierarchal
clustering outcomes estimation methods can construct in comparison to the
precision exhibited (presented through the cophenetic correlation coefficient)
in comparison to their high efficiency and effectivity in value preservation of
true and imputed values (presented through the root-mean-squared error). We
argue against the traditional approach towards the development of imputation
methods and instead, advocate towards methods that reproduce a data set's
original, natural cluster.
  By using a number of advanced imputation methods, we reveal extensive
differences between original and reconstructed clusters that could
significantly transform the interpretations of the data as a whole.
