We explore the merits of neural network boosted, principal-component-projection-based,
unsupervised data classification in single-molecule break junction
measurements, demonstrating that this method identifies highly relevant
trace classes according to the well-defined and well-visualized internal
correlations of the data set. To this end, we investigate single-molecule
structures exhibiting double molecular configurations, exploring the
role of the leading principal components in the identification of
alternative junction evolution trajectories. We show how the proper
principal component projections can be applied to separately analyze
the high- or low-conductance molecular configurations, which we exploit
in 1/f-type noise measurements on bipyridine molecules. This approach
untangles the unclear noise evolution of the entire data set, identifying
the coupling of the aromatic ring to the electrodes through the π
orbitals in two distinct conductance regions, and its subsequent uncoupling
as these configurations are stretched.