  Given data obtained under two sampling conditions, it is often of interest to
identify variables that behave differently in one condition than in the other.
We introduce a method for differential analysis of second-order behavior called
Differential Correlation Mining (DCM). The DCM method identifies differentially
correlated sets of variables, with the property that the average pairwise
correlation between variables in a set is higher under one sample condition
than the other. DCM is based on an iterative search procedure that adaptively
updates the size and elements of a candidate variable set. Updates are
performed via hypothesis testing of individual variables, based on the
asymptotic distribution of their average differential correlation. We
investigate the performance of DCM by applying it to simulated data as well as
recent experimental datasets in genomics and brain imaging.
