Anomaly pattern detection in categorical datasets

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One-Class Support Measure Machines for Group Anomaly Detection (2013, background): “Another possibility is to first identify the individually anomalous points and then find their aggregations (Das et al. 2008).”
Hierarchical Probabilistic Models for Group Anomaly Detection (2011, background): “Das et al. (2008) ﬁrst detects interesting points, and then ﬁnd subsets of the data with a high ratio of anomalous points.”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “[97] 2008 3 O N P Categorical KDDcup99 C1 APD Algorithm Lu and Tong [98] 2009 2 O N P Numeric DARPA99 C1 CUSUM-EM Qadeer et al.”
Support Measure Data Description for group anomaly detection (2015, background): “14], or estimating group anomalies by clustering point anomalies [5].”
Outlier Analysis (2013, methodology): “methods for anomaly detection are discussed in [34, 127], the first of which is information-theoretic in nature.”
Support Measure Data Description A One-Class Classifier for Group Anomaly Detection (2017, background): “Another works ignore the fact that group anomalies can be distributionbased [5].”
Group Anomaly Detection using Flexible Genre Models (2011, background): “A common way to detect point-based anomalies is to first identify anomalous points and then find their aggregations using scanning or segmentation methods [2, 3, 4].”