  We present a new data analysis perspective to determine variable importance
regardless of the underlying learning task. Traditionally, variable selection
is considered an important step in supervised learning for both classification
and regression problems. The variable selection also becomes critical when
costs associated with the data collection and storage are considerably high for
cases like remote sensing. Therefore, we propose a new methodology to select
important variables from the data by first creating dependency networks among
all variables and then ranking them (i.e. nodes) by graph centrality measures.
Selecting Top-$n$ variables according to preferred centrality measure will
yield a strong candidate subset of variables for further learning tasks e.g.
clustering. We present our tool as a Shiny app which is a user-friendly
interface development environment. We also extend the user interface for two
well-known unsupervised variable selection methods from literature for
comparison reasons.
