  In this paper we describe a method to discover frequent behavioral patterns
in event logs. We express these patterns as \emph{local process models}. Local
process model mining can be positioned in-between process discovery and episode
/ sequential pattern mining. The technique presented in this paper is able to
learn behavioral patterns involving sequential composition, concurrency, choice
and loop, like in process mining. However, we do not look at start-to-end
models, which distinguishes our approach from process discovery and creates a
link to episode / sequential pattern mining. We propose an incremental
procedure for building local process models capturing frequent patterns based
on so-called process trees. We propose five quality dimensions and
corresponding metrics for local process models, given an event log. We show
monotonicity properties for some quality dimensions, enabling a speedup of
local process model discovery through pruning. We demonstrate through a real
life case study that mining local patterns allows us to get insights in
processes where regular start-to-end process discovery techniques are only able
to learn unstructured, flower-like, models.
