  With automated measurements, large data sets can be assembled with relative
ease. Often these data sets have a large degree of variance. Nonetheless, we
hope to find groups within the data set with shared distinguishing
characteristics. Recently Albrecht, et al demonstrated a multi-parameter
vector-based classification analysis which provided a powerful means to find
clusters within a data set of conductance versus displacement traces measured
from single molecular break junctions (Lemmer, et al, Nature Communications,
2016, 7, 12922). An idealized blank tunneling trace was used as a reference to
calculate three vector variables. The authors suggested a more appropriate
reference can be chosen to better form clusters in the data. Here we propose
using a principal component of the correlation matrix calculated from the data
set to construct a fast and strategic reference trace. Principal components
form an orthogonal basis set in units of the independent variables comprising
the correlation matrix. The first principal component, the one corresponding to
the largest eigenvalue of the correlation matrix, points in the direction of
largest variance in the data. We used both a blank tunneling reference trace
and a reference trace constructed from the first principal component of the
correlation matrix to analyze a set of 4,4'-bipyridine single molecular break
junctions. The blank tunneling reference trace produced a single elongated
cluster in the vector analysis plot whereas the reference trace constructed
from the principal component distinguished two arms in the cluster. We
hypothesize that these two arms are due to groups of traces with either long or
short molecular plateaus. Statistically based sorting algorithms, like
vector-based categorization techniques, provide an objective framework to
formulate such hypothesis and aid in designing new and pointed studies to
verify them.
