  When faced with a new dataset, most practitioners begin by performing
exploratory data analysis to discover interesting patterns and characteristics
within data. Techniques such as association rule mining are commonly applied to
uncover relationships between features (attributes) of the data. However,
association rules are primarily designed for use on binary or categorical data,
due to their use of rule-based machine learning. A large proportion of
real-world data is continuous in nature, and discretisation of such data leads
to inaccurate and less informative association rules. In this paper, we propose
an alternative approach called feature relationship mining (FRM), which uses a
genetic programming approach to automatically discover symbolic relationships
between continuous or categorical features in data. To the best of our
knowledge, our proposed approach is the first such symbolic approach with the
goal of explicitly discovering relationships between features. Empirical
testing on a variety of real-world datasets shows the proposed method is able
to find high-quality, simple feature relationships which can be easily
interpreted and which provide clear and non-trivial insight into data.
