  High-volume feature-rich data sets are becoming the bread-and-butter of 21st
century astronomy but present significant challenges to scientific discovery.
In particular, identifying scientifically significant relationships between
sets of parameters is non-trivial. Similar problems in biological and
geosciences have led to the development of systems which can explore large
parameter spaces and identify potentially interesting sets of associations. In
this paper, we describe the application of automated discovery systems of
relationships to astronomical data sets, focussing on an evolutionary
programming technique and an information-theory technique. We demonstrate their
use with classical astronomical relationships - the Hertzsprung-Russell diagram
and the fundamental plane of elliptical galaxies. We also show how they work
with the issue of binary classification which is relevant to the next
generation of large synoptic sky surveys, such as LSST. We find that comparable
results to more familiar techniques, such as decision trees, are achievable.
Finally, we consider the reality of the relationships discovered and how this
can be used for feature selection and extraction.
