  The volume of data that will be produced by the next generation of
astrophysical instruments represents a significant opportunity for making
unplanned and unexpected discoveries. Conversely, finding unexpected objects or
phenomena within such large volumes of data presents a challenge that may best
be solved using computational and statistical approaches. We present the
application of a coarse-grained complexity measure for identifying interesting
observations in large astronomical data sets. This measure, which has been
termed apparent complexity, has been shown to model human intuition and
perceptions of complexity. Apparent complexity is computationally efficient to
derive and can be used to segment and identify interesting observations in very
large data sets based on their morphological complexity. We show, using data
from the Australia Telescope Large Area Survey, that apparent complexity can be
combined with clustering methods to provide an automated process for
distinguishing between images of galaxies which have been classified as having
simple and complex morphologies. The approach generalizes well when applied to
new data after being calibrated on a smaller data set, where it performs better
than tested classification methods using pixel data. This generalizability
positions apparent complexity as a suitable machine-learning feature for
identifying complex observations with unanticipated features.
