Orca is a data-driven, unsupervised anomaly detection algorithm that uses a distance-based approach. It uses a novel pruning rule that allows it to run in nearly linear time. Orca was co-developed by Stephen Bay of ISLE and Mark Schwabacher of NASA ARC. More information about Orca, including downloadable software, can be found here:
[http://stephenbay.net/orca/](http://stephenbay.net/orca/)
A conference paper about Orca can be found here:
[https://dashlink.arc.nasa.gov/paper/mining-distance-based-outliers-in-near-linear-time/](https://dashlink.arc.nasa.gov/paper/mining-distance-based-outliers-in-near-linear-time/)