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.

本文介绍了自动发现关系系统及其算法在天文数据集中的应用，着重研究了遗传编程技术和信息论技术，并展示了它们与决策树等技术的可比较性以及其对特征选择、提取的重要性，适用于也被如 LSST 等下一代大视场巡天勘测所采用的二元分类问题