With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site
visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals.
Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that
support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning
techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent
prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize
behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior
pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize
information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis.
The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and
sensor management for data extraction, relations discovery, and situation analysis of existing data.