  Practical anomaly detection requires applying numerous approaches due to the
inherent difficulty of unsupervised learning. Direct comparison between complex
or opaque anomaly detection algorithms is intractable; we instead propose a
framework for associating the scores of multiple methods. Our aim is to answer
the question: how should one measure the similarity between anomaly scores
generated by different methods? The scoring crux is the extremes, which
identify the most anomalous observations. A pair of algorithms are defined here
to be similar if they assign their highest scores to roughly the same small
fraction of observations. To formalize this, we propose a measure based on
extremal similarity in scoring distributions through a novel upper quadrant
modeling approach, and contrast it with tail and other dependence measures. We
illustrate our method with simulated and real experiments, applying spectral
methods to cluster multiple anomaly detection methods and to contrast our
similarity measure with others. We demonstrate that our method is able to
detect the clusters of anomaly detection algorithms to achieve an accurate and
robust ensemble algorithm.
