L. Allison, &
G. I. Webb
International Conference on Data Mining (ICDM 2014)
Log-linear analysis is the primary statistical approach to discovering
conditional dependencies between the variables of a dataset.
What makes a good log-linear analysis method is twofold:
high precision and statistical efficiency.
High precision means that the risk of false discoveries should
be kept very low.
Discovery efficiency means that the method should discover
actual associations with as few samples as possible.
Classical approaches to log-linear analysis make use
of χ2 tests
to control this balance between quality and complexity.
We present an information-theoretic approach to log-linear analysis.
We show that our approach
1) requires significantly fewer samples to
discover the true associations than statistical approaches -
statistical efficiency -
2) controls for the risk of false discoveries as well as
statistical approaches - high precision - and
3) can perform the discovery on datasets with hundreds of
variables on a standard desktop computer - computational efficiency.