Outlier detection is critical in real applications to prevent financial
fraud, defend network intrusions, or detecting imminent device failures. To
reduce the human effort in evaluating outlier detection results and effectively
turn the outliers into actionable insights, the users often expect a system to
automatically produce interpretable summarizations of subgroups of outlier
detection results. Unfortunately, to date no such systems exist. To fill this
gap, we propose STAIR which learns a compact set of human understandable rules
to summarize and explain the anomaly detection results. Rather than use the
classical decision tree algorithms to produce these rules, STAIR proposes a new
optimization objective to produce a small number of rules with least
complexity, hence strong interpretability, to accurately summarize the
detection results. The learning algorithm of STAIR produces a rule set by
iteratively splitting the large rules and is optimal in maximizing this
objective in each iteration. Moreover, to effectively handle high dimensional,
highly complex data sets which are hard to summarize with simple rules, we
propose a localized STAIR approach, called L-STAIR. Taking data locality into
consideration, it simultaneously partitions data and learns a set of localized
rules for each partition. Our experimental study on many outlier benchmark
datasets shows that STAIR significantly reduces the complexity of the rules
required to summarize the outlier detection results, thus more amenable for
humans to understand and evaluate, compared to the decision tree methods