  Mining the underlying patterns in gigantic and complex data is of great
importance to data analysts. In this paper, we propose a motion pattern
approach to mine frequent behaviors in trajectory data. Motion patterns,
defined by a set of highly similar flow vector groups in a spatial locality,
have been shown to be very effective in extracting dominant motion behaviors in
video sequences. Inspired by applications and properties of motion patterns, we
have designed a framework that successfully solves the general task of
trajectory clustering. Our proposed algorithm consists of four phases: flow
vector computation, motion component extraction, motion component's
reachability set creation, and motion pattern formation. For the first phase,
we break down trajectories into flow vectors that indicate instantaneous
movements. In the second phase, via a Kmeans clustering approach, we create
motion components by clustering the flow vectors with respect to their location
and velocity. Next, we create motion components' reachability set in terms of
spatial proximity and motion similarity. Finally, for the fourth phase, we
cluster motion components using agglomerative clustering with the weighted
Jaccard distance between the motion components' signatures, a set created using
path reachability. We have evaluated the effectiveness of our proposed method
in an extensive set of experiments on diverse datasets. Further, we have shown
how our proposed method handles difficulties in the general task of trajectory
clustering that challenge the existing state-of-the-art methods.
