  A simple and fast analysis method to sort large data sets into groups with
shared distinguishing characteristics is described, and applied to single
molecular break junction conductance versus electrode displacement data. The
method, based on principal component analysis, successfully sorted data sets
based on the projection of the data onto the first or second principal
component of the correlation matrix without the need to assert any specific
hypothesis about the expected features within the data. This was an improvement
on the current correlation matrix analysis approach because it sorted data
automatically, making it more objective and less time consuming, and our method
is applicable to a wide range of multivariate data sets. Here the method was
demonstrated on two systems. First, it was demonstrated on mixtures of two
molecules with identical anchor groups, similar lengths, but either a $\pi$
(high conductance) or $\sigma$ (low conductance) bridge. The mixed data was
automatically sorted into two groups containing one molecule or the other.
Second, it was demonstrated on break junction data measured with the $\pi$
bridged molecule alone. Again the method distinguished between two groups.
These groups were tentatively assigned to different geometries of the molecule
in the junction.
