  Linear regression is effective at identifying interpretable trends in a data
set, but averages out potentially different effects on subgroups within data.
We propose an iterative algorithm based on the randomized Kaczmarz (RK) method
to automatically identify subgroups in data and perform linear regression on
these groups simultaneously. We prove almost sure convergence for this method,
as well as linear convergence in expectation under certain conditions. The
result is an interpretable collection of different weight vectors for the
regressor variables that capture the different trends within data. Furthermore,
we experimentally validate our convergence results by demonstrating the method
can successfully identify two trends within simulated data.
