  We describe the applications of clustering and visualization tools using the
so-called neutral B anomalies as an example. Clustering permits parameter space
partitioning into regions that can be separated with some given measurements.
It provides a visualization of the collective dependence of all the observables
on the parameters of the problem. These methods highlight the relative
importance of different observables, and the effect of correlations, and help
to understand tensions in global fits. The tools we describe also permit a
visual inspection of high dimensional observable and parameter spaces through
both linear projections and slicing.
