  In the present era of large scale surveys, big data presents new challenges
to the discovery process for anomalous data. Such data can be indicative of
systematic errors, extreme (or rare) forms of known phenomena, or most
interestingly, truly novel phenomena which exhibit as-of-yet unobserved
behaviors. In this work we present an outlier scoring methodology to identify
and characterize the most promising unusual sources to facilitate discoveries
of such anomalous data. We have developed a data mining method based on
k-Nearest Neighbor distance in feature space to efficiently identify the most
anomalous lightcurves. We test variations of this method including using
principal components of the feature space, removing select features, the effect
of the choice of k, and scoring to subset samples. We evaluate the peformance
of our scoring on known object classes and find that our scoring consistently
scores rare (<1000) object classes higher than common classes. We have applied
scoring to all long cadence lightcurves of quarters 1 to 17 of Kepler's prime
mission and present outlier scores for all 2.8 million lightcurves for the
roughly 200k objects.
