  We consider two different data sets of syntactic parameters and we discuss
how to detect relations between parameters through a heat kernel method
developed by Belkin-Niyogi, which produces low dimensional representations of
the data, based on Laplace eigenfunctions, that preserve neighborhood
information. We analyze the different connectivity and clustering structures
that arise in the two datasets, and the regions of maximal variance in the
two-parameter space of the Belkin-Niyogi construction, which identify
preferable choices of independent variables. We compute clustering coefficients
and their variance.
