  Crystal orientation mapping experiments typically measure orientations that
are similar within grains and misorientations that are similar along grain
boundaries. Such (mis)orientation data will cluster in (mis)orientation space
and clusters are more pronounced if preferred orientations or special
orientation relationships are present. Here, cluster analysis of
(mis)orientation data is described and demonstrated using distance metrics
incorporating crystal symmetry and the density-based clustering algorithm
DBSCAN. Frequently measured (mis)orientations are identified as corresponding
to grains, grain boundaries or orientation relationships, which are visualised
both spatially and in three-dimensional (mis)orientation spaces. A new
open-source python library, orix, is also reported.
