  Correlation matrix visualization is essential for understanding the
relationships between variables in a dataset, but missing data can pose a
significant challenge in estimating correlation coefficients. In this paper, we
compare the effects of various missing data methods on the correlation plot,
focusing on two common missing patterns: random and monotone. We aim to provide
practical strategies and recommendations for researchers and practitioners in
creating and analyzing the correlation plot. Our experimental results suggest
that while imputation is commonly used for missing data, using imputed data for
plotting the correlation matrix may lead to a significantly misleading
inference of the relation between the features. We recommend using DPER, a
direct parameter estimation approach, for plotting the correlation matrix based
on its performance in the experiments.
