  XMM-Newton provides unprecedented insight into the X-ray Universe, recording
variability information for hundreds of thousands of sources. Manually
searching for interesting patterns in light curves is impractical, requiring an
automated data-mining approach for the characterization of sources.
  Straightforward fitting of temporal models to light curves is not a sure way
to identify them, especially with noisy data. We used unsupervised machine
learning to distill a large data set of light-curve parameters, revealing its
clustering structure in preparation for anomaly detection and subsequent
searches for specific source behaviors (e.g., flares, eclipses).
  Self-organizing maps (SOMs) achieve dimensionality reduction and clustering
within a single framework. They are a type of artificial neural network trained
to approximate the data with a two-dimensional grid of discrete interconnected
units, which can later be visualized on the plane. We trained our SOM on
temporal-only parameters computed from more than 100,000 detections from the
EXTraS catalog.
  The resulting map reveals that about 2500 most variable sources are clustered
based on temporal characteristics. We find distinctive regions of the SOM map
associated with flares, eclipses, dips, linear light curves, and others. Each
group contains sources that appear similar by eye. We single out a handful of
interesting sources for further study.
  The condensed view of our dataset provided by SOMs allowed us to identify
groups of similar sources, speeding up manual characterization by orders of
magnitude. Our method also highlights problems with fitting simple temporal
models to light curves and can be used to mitigate them to an extent. This will
be crucial for fully exploiting the high data volume expected from upcoming
X-ray surveys, and may also help with interpreting supervised classification
models.
