  Isolation forest or "iForest" is an intuitive and widely used algorithm for
anomaly detection that follows a simple yet effective idea: in a given data
distribution, if a threshold (split point) is selected uniformly at random
within the range of some variable and data points are divided according to
whether they are greater or smaller than this threshold, outlier points are
more likely to end up alone or in the smaller partition. The original procedure
suggested the choice of variable to split and split point within a variable to
be done uniformly at random at each step, but this paper shows that "clustered"
diverse outliers - oftentimes a more interesting class of outliers than others
- can be more easily identified by applying a non-uniformly-random choice of
variables and/or thresholds. Different split guiding criteria are compared and
some are found to result in significantly better outlier discrimination for
certain classes of outliers.
