  We propose an algorithm for detecting patterns exhibited by anomalous
clusters in high dimensional discrete data. Unlike most anomaly detection (AD)
methods, which detect individual anomalies, our proposed method detects groups
(clusters) of anomalies; i.e. sets of points which collectively exhibit
abnormal patterns. In many applications this can lead to better understanding
of the nature of the atypical behavior and to identifying the sources of the
anomalies. Moreover, we consider the case where the atypical patterns exhibit
on only a small (salient) subset of the very high dimensional feature space.
Individual AD techniques and techniques that detect anomalies using all the
features typically fail to detect such anomalies, but our method can detect
such instances collectively, discover the shared anomalous patterns exhibited
by them, and identify the subsets of salient features. In this paper, we focus
on detecting anomalous topics in a batch of text documents, developing our
algorithm based on topic models. Results of our experiments show that our
method can accurately detect anomalous topics and salient features (words)
under each such topic in a synthetic data set and two real-world text corpora
and achieves better performance compared to both standard group AD and
individual AD techniques. All required code to reproduce our experiments is
available from https://github.com/hsoleimani/ATD
