  How can we discover objects we did not know existed within the large datasets
that now abound in astronomy? We present an outlier detection algorithm that we
developed, based on an unsupervised Random Forest. We test the algorithm on
more than two million galaxy spectra from the Sloan Digital Sky Survey and
examine the 400 galaxies with the highest outlier score. We find objects which
have extreme emission line ratios and abnormally strong absorption lines,
objects with unusual continua, including extremely reddened galaxies. We find
galaxy-galaxy gravitational lenses, double-peaked emission line galaxies, and
close galaxy pairs. We find galaxies with high ionisation lines, galaxies which
host supernovae, and galaxies with unusual gas kinematics. Only a fraction of
the outliers we find were reported by previous studies that used specific and
tailored algorithms to find a single class of unusual objects. Our algorithm is
general and detects all of these classes, and many more, regardless of what
makes them peculiar. It can be executed on imaging, time-series, and other
spectroscopic data, operates well with thousands of features, is not sensitive
to missing values, and is easily parallelisable.
