  Activity profiles of terrorist groups show frequent spurts and downfalls
corresponding to changes in the underlying organizational dynamics. In
particular, it is of interest in understanding changes in attributes such as
intentions/ideology, tactics/strategies, capabilities/resources, etc., that
influence and impact the activity. The goal of this work is the quick detection
of such changes and in general, tracking of macroscopic as well as microscopic
trends in group dynamics. Prior work in this area are based on parametric
approaches and rely on time-series analysis techniques, self-exciting hurdle
models (SEHM), or hidden Markov models (HMM). While these approaches detect
spurts and downfalls reasonably accurately, they are all based on model
learning --- a task that is difficult in practice because of the "rare" nature
of terrorist attacks from a model learning perspective. In this paper, we
pursue an alternate non-parametric approach for spurt detection in activity
profiles. Our approach is based on binning the count data of terrorist activity
to form observation vectors that can be compared with each other. Motivated by
a majorization theory framework, these vectors are then transformed via certain
functionals and used in spurt classification. While the parametric approaches
often result in either a large number of missed detections of real changes or
false alarms of unoccurred changes, the proposed approach is shown to result in
a small number of missed detections and false alarms. Further, the
non-parametric nature of the approach makes it attractive for ready
applications in a practical context.
