  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 dataset. 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 dataset, identifying the coupling of
the aromatic ring to the electrodes through the $\pi$ orbitals in two distinct
conductance regions, and its subsequent uncoupling as these configurations are
stretched.
